New Upstream Release - r-cran-matchit
Ready changes
Summary
Merged new upstream version: 4.5.4 (was: 4.5.2).
Diff
diff --git a/DESCRIPTION b/DESCRIPTION
index 033dfea..e6b415f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,5 +1,5 @@
Package: MatchIt
-Version: 4.5.1
+Version: 4.5.4
Title: Nonparametric Preprocessing for Parametric Causal Inference
Description: Selects matched samples of the original treated and
control groups with similar covariate distributions -- can be
@@ -25,11 +25,12 @@ Authors@R: c(
comment = c(ORCID="0000-0003-3067-7154"))
)
Depends: R (>= 3.5.0)
-Imports: backports (>= 1.1.9), Rcpp (>= 1.0.10)
+Imports: backports (>= 1.1.9), chk (>= 0.8.1), rlang (>= 1.1.0), Rcpp,
+ utils, stats, graphics
Suggests: optmatch (>= 0.10.6), Matching, rgenoud, quickmatch (>=
0.2.1), nnet, rpart, mgcv, CBPS (>= 0.17), dbarts, randomForest
(>= 4.7-1), glmnet (>= 4.0), gbm (>= 2.1.7), cobalt (>= 4.2.3),
- boot, marginaleffects (>= 0.9.0), sandwich (>= 2.5-1),
+ boot, marginaleffects (>= 0.11.0), sandwich (>= 2.5-1),
survival, RcppProgress (>= 0.4.2), Rglpk, Rsymphony, gurobi,
knitr, rmarkdown
LinkingTo: Rcpp, RcppProgress
@@ -42,7 +43,7 @@ BugReports: https://github.com/kosukeimai/MatchIt/issues
VignetteBuilder: knitr
RoxygenNote: 7.2.3
NeedsCompilation: yes
-Packaged: 2023-02-22 17:01:39 UTC; NoahGreifer
+Packaged: 2023-06-13 17:36:36 UTC; NoahGreifer
Author: Daniel Ho [aut] (<https://orcid.org/0000-0002-2195-5469>),
Kosuke Imai [aut] (<https://orcid.org/0000-0002-2748-1022>),
Gary King [aut] (<https://orcid.org/0000-0002-5327-7631>),
@@ -51,4 +52,4 @@ Author: Daniel Ho [aut] (<https://orcid.org/0000-0002-2195-5469>),
Noah Greifer [cre, aut] (<https://orcid.org/0000-0003-3067-7154>)
Maintainer: Noah Greifer <noah.greifer@gmail.com>
Repository: CRAN
-Date/Publication: 2023-02-23 11:50:06 UTC
+Date/Publication: 2023-06-14 08:22:20 UTC
diff --git a/MD5 b/MD5
index 7b749a2..6754225 100644
--- a/MD5
+++ b/MD5
@@ -1,96 +1,98 @@
-bbadf6628065d14b0d70a8fee86938da *DESCRIPTION
+7b9725a660e87481c11680d27ff999de *DESCRIPTION
b8f810926903f4d346d9cd5d380d6251 *NAMESPACE
-2d6855784f5dddb7b6742061d348572e *NEWS.md
+ae68dcc12c2f714b373aa774aacb0cf9 *NEWS.md
793777fa54b742459d3845fea6dc0269 *R/MatchIt-package.R
-30c08f40683c3e0e2266d2748671f21c *R/RcppExports.R
-e4ac998c454c9e16a850702caa0ab1bc *R/add_s.weights.R
-c1856cef3258fa5f43d215d26f23cda2 *R/aux_functions.R
-080151a9d8b3e1cf8427c3847ec39bd3 *R/discard.R
-d250f9906ab31153825743aeeb929683 *R/dist_functions.R
-34f0ab4557071314069f55833d2a329f *R/distance2_methods.R
-1366b1c860e68337f5a79e2711a06ad1 *R/input_processing.R
+e73bbfc5fc32d3b000101d67e04e6ba8 *R/RcppExports.R
+29b2f7484bc72b8170e0ed977040332c *R/add_s.weights.R
+7e30b0aed416dd39fb0132bfa7ade082 *R/aux_functions.R
+7e1382264b2fe5b33efc682c57d0166e *R/discard.R
+37d899860114bf269ee427c0eb3f3b56 *R/dist_functions.R
+365a85d5055078150365cf1d0b2b124d *R/distance2_methods.R
+f0378d94dd62ffd3daaff0c7c45b0577 *R/get_weights_from_mm.R
+5b9e4ea0a22bb407bb8923a96d1ca133 *R/get_weights_from_subclass.R
+26bdd9ac4f5b985eb2ce92abe6c4b40e *R/input_processing.R
08e8b9e36da0e7646862a90357f5246e *R/lalonde.R
-9d516f337fe52bf2a9c71bc36c46b232 *R/match.data.R
-bd976cbc0d7764b2044e4b6d532de4c7 *R/match.qoi.R
-a7c1f0dde90ccf8ce3cc421033adac06 *R/matchit.R
-aa4b863b8ac28dce824aee9bdfae8736 *R/matchit2cardinality.R
-af80830c00844c92da8f325a171374c5 *R/matchit2cem.R
-bd362fec9b3f7acf29977dab7424cd1b *R/matchit2exact.R
-65b9eb8f1c162319912509981b4f082e *R/matchit2full.R
-7c382a95623df6b8e2fefb372f02f704 *R/matchit2genetic.R
-0c17fa560712dc20b25a992ed425816f *R/matchit2nearest.R
-c8f4a47c4afb0f56d2b4ccf59266674d *R/matchit2optimal.R
-0c1cc15490e8c1f9f3e564797d1ffa06 *R/matchit2quick.R
-ae664ec98c329eac7f305f023694563c *R/matchit2subclass.R
-707739a6e1961fe40e2dcec32cf9f0b5 *R/plot.matchit.R
-ed72f04c9f4f04d4f3456b79e57bec3c *R/plot.summary.matchit.R
-f9a75d009010e03e9486f2afb36c0b3e *R/rbind.matchdata.R
-680829e92375b69acd2de4683b365d0f *R/summary.matchit.R
-49119b5473ea8688a7c9330495cc0608 *R/weights.matrix.R
-347fcdd56372ff802f9dd607ed084187 *R/weights.subclass.R
+51f1bfed4fd9d4b0b60412d556d37500 *R/match.data.R
+8ba6b2d61691cad7a01b9a73b344dd1a *R/match.qoi.R
+e934a28d248f0281f660a98ff12da8fc *R/matchit.R
+64fa62df4ea0ad01d77788d210b0b9fc *R/matchit2cardinality.R
+be1b45ccd346aedf79629e54ff88cf4e *R/matchit2cem.R
+3f89b29cef32ada45200c1ad51b6c94f *R/matchit2exact.R
+00010cf8b7b9a08fe0774bf11344768a *R/matchit2full.R
+b6e02e8668eee91c19e44766404ae620 *R/matchit2genetic.R
+513ae084c92af4936986c4fcc06993dd *R/matchit2nearest.R
+70a8b14fe71927f071bbc63d51e2e711 *R/matchit2optimal.R
+f602efcf2dc6b84d4467f0e39e268d90 *R/matchit2quick.R
+c41e7b83345ad59b4982548fe7f3095a *R/matchit2subclass.R
+2b79887757c2b17dc0915829a481a2b8 *R/plot.matchit.R
+dfb1e12050fc1f8955e175a1bfac445a *R/plot.summary.matchit.R
+18c11004c6600c906b52e6adb1f56ada *R/rbind.matchdata.R
+352770e6a615940d63e9171c3cb54802 *R/summary.matchit.R
cf17c848081f03144256c3a7003edd4e *R/zzz.R
-80fd6dd30cc04fb4ec33135f49f4b98e *build/MatchIt.pdf
-957dc43005bfecf4a151d1322e451910 *build/vignette.rds
+92196544e7fd7540f550616a39320b1c *build/MatchIt.pdf
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0aa041b2d13d2489b60afea0103660f7 *data/lalonde.tab
2e3c4d1b48e19069607c92544fc0fc60 *inst/CITATION
8017cafd5c7d3f71e62c6d664494639c *inst/doc/MatchIt.R
-249f4c8dbd7013fd66885dc257e07925 *inst/doc/MatchIt.Rmd
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64076382a1fc5c285c5679e82ff4a3c3 *inst/doc/assessing-balance.R
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572289867ea1b8f5a2c68c079ec98db2 *inst/doc/sampling-weights.R
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3959b8f5e0ce16d378d0e100deed1f88 *inst/include/MatchIt.h
9fad12433d3f9297c7d649ab27613924 *inst/include/MatchIt_RcppExports.h
45dea246b8e6ecce342f5cd03942ccfc *man/MatchIt-package.Rd
4cf7bd8549b40427e6094fc25ec62e7e *man/add_s.weights.Rd
c030f13edb1d39bac606d51b7e8886e5 *man/distance.Rd
3425523d337ca3d85f23b912c098b0b6 *man/figures/README-unnamed-chunk-4-1.png
-eac97eb00b2f482b0db0f3efc91da72b *man/figures/README-unnamed-chunk-5-1.png
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de9f73d1fb2d4fd20aafb80bf237aff6 *man/figures/logo.png
33d63bff868074d624d467c8761ba28a *man/lalonde.Rd
7f976dfae2d3a4b72d42af82de4394dd *man/macros/macros.Rd
-2ec9ed41fab6bfa0e22035c8646be9ba *man/mahalanobis_dist.Rd
-00387e8fe8adcb4d7071c79c8e789483 *man/match.data.Rd
-a87a7a96a9ebacb2d526715dcc391236 *man/matchit.Rd
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121da38eeb742fe7d39bd448ed12d30e *man/method_cardinality.Rd
-6a4fafdb7219b4f186521f742bf7124a *man/method_cem.Rd
+051a6582e5c1df7b11cf43a346afc40a *man/method_cem.Rd
9a174ae3580db0a5a7659a1e80b64108 *man/method_exact.Rd
3f7b83f491e5f7fed1f2c6594febb1b0 *man/method_full.Rd
-abbccb091ff29431e0060c5235d7936c *man/method_genetic.Rd
-332492a752129db3be0dcfcfae1b767f *man/method_nearest.Rd
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f60d74ba184e2143dd97a0ba888785e6 *man/method_optimal.Rd
-adbb259780e0f6cf3965c89b1e87aa24 *man/method_quick.Rd
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a578e148e0d96316847bb1d55e4b83bd *man/method_subclass.Rd
-62aa444dec926cfbc4b18629bd532785 *man/plot.matchit.Rd
+30336ad507362eb1a7bd0c920756705d *man/plot.matchit.Rd
ecebe26dc81963f20c9af8d631e942ba *man/plot.summary.matchit.Rd
dbadc82501ca1f2c57b124678ec30b22 *man/rbind.matchdata.Rd
662bf3dc22fe47e0b1dc83b705ab219a *man/summary.matchit.Rd
c89d6b95d18721894ab45341d0fbc7a6 *src/Makevars
c89d6b95d18721894ab45341d0fbc7a6 *src/Makevars.win
-b3d242d8f95352f757bf54e857f2c898 *src/RcppExports.cpp
+71e77c24645696491da10856adb035fd *src/RcppExports.cpp
7b5d305b22dee4cb0336333f0c5106d4 *src/dist_to_matrix.cpp
ce7f68047fb68bf99a7c51dbecca4e0d *src/energy_match.cpp
414df2ed3c722a2921743e7452def7e7 *src/internal.cpp
67472d1e971b4a42b9c44ed32a2cc6b9 *src/internal.h
-c6442dfc1b27939850c7e97218cbd088 *src/nn_matchC.cpp
-e6878b3bcdf671bd9f7c77d2861c86c1 *src/nn_matchC_vec.cpp
+98e0b5e3095467cb81fdf1f1d29494da *src/nn_matchC.cpp
+03f442f36419e7dad1e2c9ec3cf6dc35 *src/nn_matchC_closest.cpp
+656f1186b46c9625fe7e8f3c4c06c3ca *src/nn_matchC_vec.cpp
beff9f881bf823963f46e940031df8c7 *src/pairdistC.cpp
9af6c99cb065d1c45625b1392c351f90 *src/subclass2mm.cpp
56ee747fd113623d8f1226795d25f45a *src/tabulateC.cpp
385f6bba27cbce19ac7255b7f8496a3e *src/weights_matrixC.cpp
-249f4c8dbd7013fd66885dc257e07925 *vignettes/MatchIt.Rmd
-f8eee89c483a2183bc0ec3b239ddfe51 *vignettes/assessing-balance.Rmd
-41e66ce1474c1b865d7a2998dc0aabaa *vignettes/estimating-effects.Rmd
-c5d00cf7e39fed812e1aa454d9fbfa41 *vignettes/matching-methods.Rmd
-e553d5f87c30d0c49b699bd6091e0307 *vignettes/references.bib
+4d22a9c2372c3e1fbd9b3e4ef3eb77eb *vignettes/MatchIt.Rmd
+384d7a1673f2b41b6c6e438df432ebce *vignettes/assessing-balance.Rmd
+a78a80dc6b9572c4369b3c2db32888d7 *vignettes/estimating-effects.Rmd
+13e48f8f2449ceb1a57ba5e43f664048 *vignettes/matching-methods.Rmd
+47d2a6e6a5ed245d5ecbbdd55e34c796 *vignettes/references.bib
e41ffdb44122d799d420bdb9240c4d18 *vignettes/sampling-weights.Rmd
diff --git a/NEWS.md b/NEWS.md
index 7c2eac8..9a035a5 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -6,6 +6,42 @@ output:
`MatchIt` News and Updates
======
+# MatchIt 4.5.4
+
+* With `method = "nearest"`, `m.order` can now be set to `"closest"` to request that the closest potential pairs are matched first. This can be used whether a propensity score is used or not.
+
+* Fixed bugs when `distance = NULL` and no covariates are specified in `matchit()`.
+
+* Changed "empirical cumulative density function" to "empirical cumulative distribution function" in documentation. (#166)
+
+* Fixed a bug where calipers would not work properly on some systems. Thanks to Bill Dunlap for the solution. (#163)
+
+* Fixed a bug when `.` was present in formulas. Thanks to @dmolitor. (#167)
+
+* Fixed a bug when nearest neighbor matching for the ATC with `distance` supplied as a numeric distance matrix.
+
+# MatchIt 4.5.3
+
+* Error messages have been improved using `chk` and `rlang`, which are now dependencies.
+
+* Fixed a bug when using `method = "nearest"` with `replace = TRUE` and `ratio` greater than 1. Thanks to Julia Kretschmann. (#159)
+
+* Fixed a bug when using `method = "nearest"` with `exact` and `ratio` greater than 1. Thanks to Sarah Conner.
+
+* Fixed a bug that would occur due to numerical imprecision in `plot.matchit()`. Thanks to @hkmztrk. (#158)
+
+* Fixed bugs when using `method = "cem"` where a covariate was to be omitted from coarsening. Thanks to @jfhelmer. (#160)
+
+* Fixed some typos in the vignettes. Thanks to @fBedecarrats. (#156)
+
+* Updated vignettes to use `marginaleffects` v0.11.0 syntax.
+
+# MatchIt 4.5.2
+
+* Fixed a bug when using `method = "quick"` with `exact` specified. Thanks to @m-marquis. (#149)
+
+* Improved performance and fixed some bugs when using `exact` in cases where some strata contain units from only one treatment group. Thanks to @m-marquis and others for pointing these out. (#151)
+
# MatchIt 4.5.1
* Nearest neighbor matching now uses a much faster algorithm (up to 6x times faster) when `distance` is a propensity score and `mahvars` is not specified. Differences in sort order might cause results to differ from previous versions if there are units with identical propensity scores.
@@ -166,7 +202,7 @@ output:
* A spurious warning that would appear when using a large `ratio` with `replace = TRUE` and `method = "nearest"` no longer appears.
-* Fixed a bug when trying to supply `distance` as a labelled numeric vector (e.g., resulting from `haven`).
+* Fixed a bug when trying to supply `distance` as a labeled numeric vector (e.g., resulting from `haven`).
* Fixed some typos in the documentation and vignettes.
diff --git a/R/RcppExports.R b/R/RcppExports.R
index 3bf983e..45310b7 100644
--- a/R/RcppExports.R
+++ b/R/RcppExports.R
@@ -9,6 +9,10 @@ nn_matchC <- function(treat_, ord_, ratio, discarded, reuse_max, distance_ = NUL
.Call(`_MatchIt_nn_matchC`, treat_, ord_, ratio, discarded, reuse_max, distance_, distance_mat_, exact_, caliper_dist_, caliper_covs_, caliper_covs_mat_, mah_covs_, antiexact_covs_, unit_id_, disl_prog)
}
+nn_matchC_closest <- function(distance_mat, treat, ratio, discarded, reuse_max, exact_ = NULL, caliper_dist_ = NULL, caliper_covs_ = NULL, caliper_covs_mat_ = NULL, antiexact_covs_ = NULL, unit_id_ = NULL, disl_prog = FALSE) {
+ .Call(`_MatchIt_nn_matchC_closest`, distance_mat, treat, ratio, discarded, reuse_max, exact_, caliper_dist_, caliper_covs_, caliper_covs_mat_, antiexact_covs_, unit_id_, disl_prog)
+}
+
nn_matchC_vec <- function(treat_, ord_, ratio_, discarded_, reuse_max, distance_, exact_ = NULL, caliper_dist_ = NULL, caliper_covs_ = NULL, caliper_covs_mat_ = NULL, antiexact_covs_ = NULL, unit_id_ = NULL, disl_prog = FALSE) {
.Call(`_MatchIt_nn_matchC_vec`, treat_, ord_, ratio_, discarded_, reuse_max, distance_, exact_, caliper_dist_, caliper_covs_, caliper_covs_mat_, antiexact_covs_, unit_id_, disl_prog)
}
diff --git a/R/add_s.weights.R b/R/add_s.weights.R
index ca7443d..7754a25 100644
--- a/R/add_s.weights.R
+++ b/R/add_s.weights.R
@@ -59,7 +59,7 @@ add_s.weights <- function(m,
s.weights = NULL,
data = NULL) {
- if (!inherits(m, "matchit")) stop("'m' must be a matchit object, the output of a call to matchit().", call. = FALSE)
+ chk::chk_is(m, "matchit")
if (!is.null(s.weights)) {
if (!is.numeric(s.weights)) {
@@ -70,43 +70,45 @@ add_s.weights <- function(m,
env <- parent.frame()
}
data <- eval(m$call$data, envir = env)
- if (length(data) == 0) stop("A dataset could not be found. Please supply an argument to 'data' containing the original dataset used in the matching.", call. = FALSE)
+ if (length(data) == 0) {
+ .err("a dataset could not be found. Please supply an argument to `data` containing the original dataset used in the matching")
+ }
}
else {
if (!is.data.frame(data)) {
if (is.matrix(data)) data <- as.data.frame.matrix(data)
- else stop("'data' must be a data frame.", call. = FALSE)
+ else .err("`data` must be a data frame")
}
if (nrow(data) != length(m$treat)) {
- stop("'data' must have as many rows as there were units in the original call to matchit().", call. = FALSE)
+ .err("`data` must have as many rows as there were units in the original call to `matchit()`")
}
}
if (is.character(s.weights)) {
if (is.null(data) || !is.data.frame(data)) {
- stop("If 's.weights' is specified a string, a data frame containing the named variable must be supplied to 'data'.", call. = FALSE)
+ .err("if `s.weights` is specified a string, a data frame containing the named variable must be supplied to `data`")
}
if (!all(s.weights %in% names(data))) {
- stop("The name supplied to 's.weights' must be a variable in 'data'.", call. = FALSE)
+ .err("the name supplied to `s.weights` must be a variable in `data`")
}
s.weights.form <- reformulate(s.weights)
s.weights <- model.frame(s.weights.form, data, na.action = "na.pass")
- if (ncol(s.weights) != 1) stop("'s.weights' can only contain one named variable.", call. = FALSE)
+ if (ncol(s.weights) != 1) .err("`s.weights` can only contain one named variable")
s.weights <- s.weights[[1]]
}
- else if (inherits(s.weights, "formula")) {
- s.weights.form <- update(s.weights, NULL ~ .)
+ else if (rlang::is_formula(s.weights)) {
+ s.weights.form <- update(terms(s.weights, data = data), NULL ~ .)
s.weights <- model.frame(s.weights.form, data, na.action = "na.pass")
- if (ncol(s.weights) != 1) stop("'s.weights' can only contain one named variable.", call. = FALSE)
+ if (ncol(s.weights) != 1) .err("`s.weights` can only contain one named variable")
s.weights <- s.weights[[1]]
}
else {
- stop("'s.weights' must be supplied as a numeric vector, string, or one-sided formula.", call. = FALSE)
+ .err("`s.weights` must be supplied as a numeric vector, string, or one-sided formula")
}
}
- if (anyNA(s.weights)) stop("Missing values are not allowed in 's.weights'.", call. = FALSE)
- if (length(s.weights) != length(m$treat)) stop("'s.weights' must be the same length as the treatment vector.", call. = FALSE)
+ chk::chk_not_any_na(s.weights)
+ if (length(s.weights) != length(m$treat)) .err("`s.weights` must be the same length as the treatment vector")
names(s.weights) <- names(m$treat)
@@ -117,5 +119,5 @@ add_s.weights <- function(m,
m$nn <- nn(m$treat, m$weights, m$discarded, s.weights)
}
- return(m)
+ m
}
diff --git a/R/aux_functions.R b/R/aux_functions.R
index 3959cbb..54ffa38 100644
--- a/R/aux_functions.R
+++ b/R/aux_functions.R
@@ -21,8 +21,8 @@ subclass_scoot <- function(sub, treat, x, min.n = 1) {
original.order)
if (any(table(treat) < nsub * min.n)) {
- stop(sprintf("Not enough units to fit treated and control %s in each subclass.",
- ngettext(min.n, "unit", "units")), call. = FALSE)
+ .err(sprintf("not enough units to fit %s treated and control %s in each subclass",
+ min.n, ngettext(min.n, "unit", "units")))
}
for (t in unique.treat) {
@@ -80,25 +80,7 @@ subclass_scoot <- function(sub, treat, x, min.n = 1) {
sub <- sub[names(sub)]
}
- return(sub)
-}
-
-#Function to check if package is installed. From WeightIt.
-check.package <- function(package.name, alternative = FALSE) {
- packages.not.installed <- package.name[!vapply(package.name, requireNamespace, logical(1L),
- quietly = TRUE)]
- if (length(packages.not.installed) > 0) {
- if (alternative) return(FALSE)
- else {
- plural <- length(packages.not.installed) > 1
- stop(paste0("Package", if (plural) "s " else " ",
- word_list(packages.not.installed, quotes = 1, is.are = TRUE),
- " needed for this function to work. Please install ",
- if (plural) "them" else "it","."),
- call. = FALSE)
- }
- }
- else return(invisible(TRUE))
+ sub
}
#Create info component of matchit object
@@ -192,14 +174,14 @@ info.to.distance <- function(info) {
if (linear) dist <- paste(dist, "and linearized")
- return(dist)
+ dist
}
#Function to turn a vector into a string with "," and "and" or "or" for clean messages. 'and.or'
#controls whether words are separated by "and" or "or"; 'is.are' controls whether the list is
#followed by "is" or "are" (to avoid manually figuring out if plural); quotes controls whether
#quotes should be placed around words in string. From WeightIt.
-word_list <- function(word.list = NULL, and.or = c("and", "or"), is.are = FALSE, quotes = FALSE) {
+word_list <- function(word.list = NULL, and.or = "and", is.are = FALSE, quotes = FALSE) {
#When given a vector of strings, creates a string of the form "a and b"
#or "a, b, and c"
#If is.are, adds "is" or "are" appropriately
@@ -223,13 +205,13 @@ word_list <- function(word.list = NULL, and.or = c("and", "or"), is.are = FALSE,
attr(out, "plural") <- FALSE
}
else {
- and.or <- match_arg(and.or)
+ and.or <- match_arg(and.or, c("and", "or"))
if (L == 2) {
- out <- paste(word.list, collapse = paste0(" ", and.or," "))
+ out <- paste(word.list, collapse = paste0(" ", and.or, " "))
}
else {
- out <- paste(paste(word.list[seq_len(L-1)], collapse = ", "),
- word.list[L], sep = paste0(", ", and.or," "))
+ out <- paste(paste(word.list[seq_len(L - 1)], collapse = ", "),
+ word.list[L], sep = paste0(", ", and.or, " "))
}
if (is.are) out <- paste(out, "are")
@@ -237,25 +219,36 @@ word_list <- function(word.list = NULL, and.or = c("and", "or"), is.are = FALSE,
}
}
- return(out)
+
+ out
}
-#Add quotation marks around a string.
+#Add quotes to a string
add_quotes <- function(x, quotes = 2L) {
- if (!isFALSE(quotes)) {
- if (isTRUE(quotes) || as.integer(quotes) == 2L) x <- sprintf('"%s"', x)
- else if (as.integer(quotes) == 1L) x <- sprintf("'%s'", x)
- else stop("'quotes' must be boolean, 1, or 2.")
+ if (isFALSE(quotes)) return(x)
+
+ if (isTRUE(quotes)) quotes <- 2
+
+ if (chk::vld_string(quotes)) x <- paste0(quotes, x, quotes)
+ else if (chk::vld_whole_number(quotes)) {
+ if (as.integer(quotes) == 0) return(x)
+ else if (as.integer(quotes) == 1) x <- paste0("\'", x, "\'")
+ else if (as.integer(quotes) == 2) x <- paste0("\"", x, "\"")
+ else stop("`quotes` must be boolean, 1, 2, or a string.")
}
+ else {
+ stop("`quotes` must be boolean, 1, 2, or a string.")
+ }
+
x
}
-#More informative and cleaner version of base::match.arg. From WeightIt.
+#More informative and cleaner version of base::match.arg(). Uses chk.
match_arg <- function(arg, choices, several.ok = FALSE) {
#Replaces match.arg() but gives cleaner error message and processing
#of arg.
if (missing(arg))
- stop("No argument was supplied to match_arg.", call. = FALSE)
+ stop("No argument was supplied to match_arg.")
arg.name <- deparse1(substitute(arg), width.cutoff = 500L)
if (missing(choices)) {
@@ -264,28 +257,23 @@ match_arg <- function(arg, choices, several.ok = FALSE) {
envir = sys.frame(sysP))
}
- if (is.null(arg))
- return(choices[1L])
- else if (!is.character(arg))
- stop(sprintf("The argument to '%s' must be NULL or a character vector", arg.name), call. = FALSE)
- if (!several.ok) {
- if (identical(arg, choices))
- return(arg[1L])
- if (length(arg) > 1L)
- stop(sprintf("The argument to '%s' must be of length 1", arg.name), call. = FALSE)
+ if (length(arg) == 0) return(choices[1L])
+
+ if (several.ok) {
+ chk::chk_character(arg, add_quotes(arg.name, "`"))
+ }
+ else {
+ chk::chk_string(arg, add_quotes(arg.name, "`"))
+ if (identical(arg, choices)) return(arg[1L])
}
- else if (length(arg) == 0)
- stop(sprintf("The argument to '%s' must be of length >= 1", arg.name), call. = FALSE)
i <- pmatch(arg, choices, nomatch = 0L, duplicates.ok = TRUE)
if (all(i == 0L))
- stop(sprintf("The argument to '%s' should be %s %s.",
+ .err(sprintf("the argument to `%s` should be %s%s.",
arg.name, ngettext(length(choices), "", if (several.ok) "at least one of " else "one of "),
- word_list(choices, and.or = "or", quotes = 2)),
- call. = FALSE)
+ word_list(choices, and.or = "or", quotes = 2)))
i <- i[i > 0L]
- if (!several.ok && length(i) > 1)
- stop("There is more than one match in 'match_arg'")
+
choices[i]
}
@@ -294,7 +282,7 @@ match_arg <- function(arg, choices, several.ok = FALSE) {
binarize <- function(variable, zero = NULL, one = NULL) {
var.name <- deparse1(substitute(variable))
if (length(unique(variable)) > 2) {
- stop(sprintf("Cannot binarize %s: more than two levels.", var.name), call. = FALSE)
+ stop(sprintf("Cannot binarize %s: more than two levels.", var.name))
}
if (is.character(variable) || is.factor(variable)) {
variable <- factor(variable, nmax = 2)
@@ -320,12 +308,12 @@ binarize <- function(variable, zero = NULL, one = NULL) {
}
else {
if (one %in% unique.vals) return(setNames(as.integer(variable == one), names(variable)))
- else stop("The argument to 'one' is not the name of a level of variable.", call. = FALSE)
+ else stop("The argument to 'one' is not the name of a level of variable.")
}
}
else {
if (zero %in% unique.vals) return(setNames(as.integer(variable != zero), names(variable)))
- else stop("The argument to 'zero' is not the name of a level of variable.", call. = FALSE)
+ else stop("The argument to 'zero' is not the name of a level of variable.")
}
}
@@ -365,7 +353,7 @@ exactify <- function(X, nam = NULL, sep = "|", include_vars = FALSE) {
can_str2num <- function(x) {
nas <- is.na(x)
suppressWarnings(x_num <- as.numeric(as.character(x[!nas])))
- return(!anyNA(x_num))
+ !anyNA(x_num)
}
#Cleanly coerces a character vector to numeric; best to use after can_str2num()
@@ -373,7 +361,7 @@ str2num <- function(x) {
nas <- is.na(x)
suppressWarnings(x_num <- as.numeric(as.character(x)))
is.na(x_num)[nas] <- TRUE
- return(x_num)
+ x_num
}
#Capitalize first letter of string
@@ -396,8 +384,9 @@ round_df_char <- function(df, digits, pad = "0", na_vals = "") {
#lines up. Should be "0" or " "; "" (the empty string) un-aligns decimals.
#na_vals is what NA should print as.
+ if (NROW(df) == 0 || NCOL(df) == 0) return(as.matrix(df))
if (!is.data.frame(df)) df <- as.data.frame.matrix(df, stringsAsFactors = FALSE)
- if (NROW(df) == 0 || NCOL(df) == 0) return(df)
+
rn <- rownames(df)
cn <- colnames(df)
@@ -443,9 +432,7 @@ round_df_char <- function(df, digits, pad = "0", na_vals = "") {
if (length(rn) > 0) rownames(df) <- rn
if (length(cn) > 0) names(df) <- cn
- df <- as.matrix(df)
-
- return(df)
+ as.matrix(df)
}
#Generalized inverse; port of MASS::ginv()
@@ -453,7 +440,7 @@ generalized_inverse <- function(sigma) {
sigmasvd <- svd(sigma)
pos <- sigmasvd$d > max(1e-8 * sigmasvd$d[1L], 0)
sigma_inv <- sigmasvd$v[, pos, drop = FALSE] %*% (sigmasvd$d[pos]^-1 * t(sigmasvd$u[, pos, drop = FALSE]))
- return(sigma_inv)
+ sigma_inv
}
#Get covariates (RHS) vars from formula
@@ -481,7 +468,7 @@ get.covs.matrix <- function(formula = NULL, data = NULL) {
X <- X[,-1,drop=FALSE]
attr(X, "assign") <- assign
- return(X)
+ X
}
#Extracts and names the "assign" attribute from get.covs.matrix()
@@ -519,7 +506,7 @@ mm2subclass <- function(mm, treat) {
subclass <- setNames(factor(subclass, nmax = length(ind1)), lab)
levels(subclass) <- seq_len(nlevels(subclass))
- return(subclass)
+ subclass
}
#(Weighted) variance that uses special formula for binary variables
@@ -531,12 +518,11 @@ wvar <- function(x, bin.var = NULL, w = NULL) {
mx <- sum(w * x) #weighted mean
if (bin.var) {
- mx*(1-mx)
- }
- else {
- #Reliability weights variance; same as cov.wt()
- sum(w * (x - mx)^2)/(1 - sum(w^2))
+ return(mx * (1 - mx))
}
+
+ #Reliability weights variance; same as cov.wt()
+ sum(w * (x - mx)^2)/(1 - sum(w^2))
}
#Weighted mean faster than weighted.mean()
@@ -549,15 +535,15 @@ wm <- function(x, w = NULL, na.rm = TRUE) {
}
return(sum(x)/length(x))
}
- else {
- if (anyNA(x) || anyNA(w)) {
- if (!na.rm) return(NA_real_)
- nas <- which(is.na(x) | is.na(w))
- x <- x[-nas]
- w <- w[-nas]
- }
- return(sum(x*w)/sum(w))
+
+ if (anyNA(x) || anyNA(w)) {
+ if (!na.rm) return(NA_real_)
+ nas <- which(is.na(x) | is.na(w))
+ x <- x[-nas]
+ w <- w[-nas]
}
+
+ sum(x*w)/sum(w)
}
#Pooled within-group (weighted) covariance by group-mean centering covariates. Used
@@ -565,6 +551,7 @@ wm <- function(x, w = NULL, na.rm = TRUE) {
pooled_cov <- function(X, t, w = NULL) {
unique_t <- unique(t)
if (is.null(dim(X))) X <- matrix(X, nrow = length(X))
+
if (is.null(w)) {
n <- nrow(X)
for (i in unique_t) {
@@ -573,18 +560,16 @@ pooled_cov <- function(X, t, w = NULL) {
X[in_t, j] <- X[in_t, j] - mean(X[in_t, j])
}
}
- pooled_cov <- cov(X)*(n-1)/(n-length(unique_t))
+ return(cov(X)*(n-1)/(n-length(unique_t)))
}
- else {
- for (i in unique_t) {
- in_t <- which(t == i)
- for (j in seq_len(ncol(X))) {
- X[in_t, j] <- X[in_t, j] - wm(X[in_t, j], w[in_t])
- }
+
+ for (i in unique_t) {
+ in_t <- which(t == i)
+ for (j in seq_len(ncol(X))) {
+ X[in_t, j] <- X[in_t, j] - wm(X[in_t, j], w[in_t])
}
- pooled_cov <- cov.wt(X, w)$cov
}
- return(pooled_cov)
+ cov.wt(X, w)$cov
}
pooled_sd <- function(X, t, w = NULL, bin.var = NULL, contribution = "proportional") {
@@ -640,13 +625,14 @@ pooled_sd <- function(X, t, w = NULL, bin.var = NULL, contribution = "proportion
for (i in unique_t) {
x[t==i] <- x[t==i] - wm(x[t==i], w[t==i])
}
- w_ <- w/sum(w)
+ w_ <- .make_sum_to_1(w)
return(sum(w_ * x^2)/(1 - sum(w_^2)))
}
}
}, numeric(1L))
}
- return(sqrt(pooled_var))
+
+ setNames(sqrt(pooled_var), colnames(X))
}
#Effective sample size
@@ -671,7 +657,7 @@ nn <- function(treat, weights, discarded = NULL, s.weights = NULL) {
n["Unmatched",] <- c(sum(treat==0 & weights==0 & !discarded), sum(treat==1 & weights==0 & !discarded))
n["Discarded",] <- c(sum(treat==0 & discarded), sum(treat==1 & discarded))
- return(n)
+ n
}
#Compute subclass sample sizes
@@ -695,7 +681,7 @@ qn <- function(treat, subclass, discarded = NULL) {
qn <- cbind(qn, rowSums(qn))
colnames(qn)[ncol(qn)] <- "All"
- return(qn)
+ qn
}
#Faster diff()
@@ -703,7 +689,133 @@ diff1 <- function(x) {
x[-1] - x[-length(x)]
}
-#Check if is a whole number
-is_whole_number <- function(x) {
- abs(x - round(x)) < .Machine$double.eps
+#cumsum() for probabilities to ensure they are between 0 and 1
+.cumsum_prob <- function(x) {
+ s <- cumsum(x)
+ s / s[length(s)]
+}
+
+#Make vector sum to 1, optionally by group
+.make_sum_to_1 <- function(x, by = NULL) {
+ if (is.null(by)) {
+ return(x / sum(x))
+ }
+
+ for (i in unique(by)) {
+ in_i <- which(by == i)
+ x[in_i] <- x[in_i] / sum(x[in_i])
+ }
+
+ x
+}
+
+#Make vector sum to n (average of 1), optionally by group
+.make_sum_to_n <- function(x, by = NULL) {
+ if (is.null(by)) {
+ return(length(x) * x / sum(x))
+ }
+
+ for (i in unique(by)) {
+ in_i <- which(by == i)
+ x[in_i] <- length(in_i) * x[in_i] / sum(x[in_i])
+ }
+
+ x
+}
+
+#Functions for error handling; based on chk and rlang
+pkg_caller_call <- function(start = 1) {
+ package.funs <- c(getNamespaceExports(utils::packageName()),
+ .getNamespaceInfo(asNamespace(utils::packageName()), "S3methods")[, 3])
+ k <- start #skip checking pkg_caller_call()
+ e_max <- start
+ while (!is.null(e <- rlang::caller_call(k))) {
+ if (!is.null(n <- rlang::call_name(e)) &&
+ n %in% package.funs) e_max <- k
+ k <- k + 1
+ }
+ rlang::caller_call(e_max)
+}
+
+.err <- function(...) {
+ chk::err(..., call = pkg_caller_call(start = 2))
+}
+.wrn <- function(...) {
+ chk::wrn(...)
+}
+.msg <- function(...) {
+ chk::msg(...)
+}
+
+#De-bugged version of chk::chk_null_or()
+.chk_null_or <- function(x, chk, ..., x_name = NULL) {
+ if (is.null(x_name)) {
+ x_name <- deparse1(substitute(x))
+ }
+
+ x_name <- add_quotes(x_name, "`")
+
+ if (is.null(x)) {
+ return(invisible(x))
+ }
+
+ tryCatch(chk(x, ..., x_name = x_name),
+ error = function(e) {
+ msg <- sub("[.]$", " or `NULL`.",
+ conditionMessage(e))
+ chk::err(msg, .subclass = "chk_error")
+ })
+}
+
+.chk_formula <- function(x, sides = NULL, x_name = NULL) {
+ if (is.null(sides)) {
+ if (rlang::is_formula(x)) {
+ return(invisible(x))
+ }
+ if (is.null(x_name)) {
+ x_name <- chk::deparse_backtick_chk(substitute(x))
+ }
+ chk::abort_chk(x_name, " must be a formula",
+ x = x)
+ }
+ else if (sides == 1) {
+ if (rlang::is_formula(x, lhs = FALSE)) {
+ return(invisible(x))
+ }
+ if (is.null(x_name)) {
+ x_name <- chk::deparse_backtick_chk(substitute(x))
+ }
+ chk::abort_chk(x_name, " must be a formula with no left-hand side",
+ x = x)
+ }
+ else if (sides == 2) {
+ if (rlang::is_formula(x, lhs = TRUE)) {
+ return(invisible(x))
+ }
+ if (is.null(x_name)) {
+ x_name <- chk::deparse_backtick_chk(substitute(x))
+ }
+ chk::abort_chk(x_name, " must be a formula with a left-hand side",
+ x = x)
+ }
+ else stop("`sides` must be NULL, 1, or 2")
+}
+
+#Function to capture and print errors and warnings better
+matchit_try <- function(expr, from = NULL, dont_warn_if = NULL) {
+ tryCatch({
+ withCallingHandlers({
+ expr
+ },
+ warning = function(w) {
+ if (is.null(dont_warn_if) || !grepl(dont_warn_if, conditionMessage(w), fixed = TRUE)) {
+ if (is.null(from)) .wrn(conditionMessage(w), tidy = FALSE)
+ else .wrn(sprintf("(from %s) %s", from, conditionMessage(w)), tidy = FALSE)
+ }
+ invokeRestart("muffleWarning")
+ })},
+ error = function(e) {
+ if (is.null(from)) .err(conditionMessage(e), tidy = FALSE)
+ else .err(sprintf("(from %s) %s", from, conditionMessage(e)), tidy = FALSE)
+ })
}
\ No newline at end of file
diff --git a/R/discard.R b/R/discard.R
index ccfda02..764673e 100644
--- a/R/discard.R
+++ b/R/discard.R
@@ -4,64 +4,64 @@ discard <- function(treat, pscore = NULL, option = NULL) {
if (length(option) == 0){
# keep all units
- discarded <- rep(FALSE, n.obs)
+ return(setNames(rep(FALSE, n.obs), names(treat)))
}
- else if (is.logical(option) && length(option) == n.obs && !anyNA(option)) {
+
+ if (is.logical(option) && length(option) == n.obs && !anyNA(option)) {
# user input
return(setNames(option, names(treat)))
}
- else if (length(option) > 1 || !is.character(option)) {
- stop("'discard' must be \"none\", \"both\", \"control\", \"treated\" or a logical vector of observations to discard.", call. = FALSE)
+
+ if (!chk::vld_string(option)) {
+ .err('`discard` must be "none", "both", "control", "treated" or a logical vector of observations to discard')
}
- else {
- option <- match_arg(option, c("none", "both", "control", "treated"))
- if (option == "none"){
- # keep all units
- discarded <- rep(FALSE, n.obs)
- }
- else {
- if (is.null(pscore)) {
- stop("'discard' must be a logical vector or \"none\" in the absence of a propensity score.", call. = FALSE)
- }
- else if (is.matrix(pscore)) {
- stop("'discard' must be a logical vector or \"none\" when 'distance' is supplied as a matrix.", call. = FALSE)
- }
+ option <- match_arg(option, c("none", "both", "control", "treated"))
+
+ if (option == "none") {
+ # keep all units
+ return(setNames(rep(FALSE, n.obs), names(treat)))
+ }
- pmax0 <- max(pscore[treat==0])
- pmax1 <- max(pscore[treat==1])
- pmin0 <- min(pscore[treat==0])
- pmin1 <- min(pscore[treat==1])
+ if (is.null(pscore)) {
+ .err('`discard` must be a logical vector or "none" in the absence of a propensity score')
+ }
- if (option == "both") # discard units outside of common support
- discarded <- (pscore < max(pmin0, pmin1) | pscore > min(pmax0, pmax1))
- else if (option == "control") # discard control units only
- discarded <- (pscore < pmin1 | pscore > pmax1)
- else if (option == "treated") # discard treated units only
- discarded <- (pscore < pmin0 | pscore > pmax0)
- }
- # NOTE: WhatIf package has been removed from CRAN, so hull options won't work
- # else if (option %in% c("hull.control", "hull.treat", "hull.both")) {
- # ## convex hull stuff
- # check.package("WhatIf")
- # X <- model.matrix(reformulate(names(covs), intercept = FALSE), data = covs,
- # contrasts.arg = lapply(Filter(is.factor, covs),
- # function(x) contrasts(x, contrasts = nlevels(x) == 1)))
- # discarded <- rep(FALSE, n.obs)
- # if (option == "hull.control"){ # discard units not in T convex hull
- # wif <- WhatIf::whatif(cfact = X[treat==0,], data = X[treat==1,])
- # discarded[treat==0] <- !wif$in.hull
- # } else if (option == "hull.treat") {
- # wif <- WhatIf::whatif(cfact = X[treat==1,], data = X[treat==0,])
- # discarded[treat==1] <- !wif$in.hull
- # } else if (option == "hull.both"){ # discard units not in T&C convex hull
- # wif <- WhatIf::whatif(cfact = cbind(1-treat, X), data = cbind(treat, X))
- # discarded <- !wif$in.hull
- # }
- # }
+ if (is.matrix(pscore)) {
+ .err('`discard` must be a logical vector or "none" when `distance` is supplied as a matrix')
}
- names(discarded) <- names(treat)
+ pmax0 <- max(pscore[treat==0])
+ pmax1 <- max(pscore[treat==1])
+ pmin0 <- min(pscore[treat==0])
+ pmin1 <- min(pscore[treat==1])
+
+ if (option == "both") # discard units outside of common support
+ discarded <- (pscore < max(pmin0, pmin1) | pscore > min(pmax0, pmax1))
+ else if (option == "control") # discard control units only
+ discarded <- (pscore < pmin1 | pscore > pmax1)
+ else if (option == "treated") # discard treated units only
+ discarded <- (pscore < pmin0 | pscore > pmax0)
+
+ # NOTE: WhatIf package has been removed from CRAN, so hull options won't work
+ # else if (option %in% c("hull.control", "hull.treat", "hull.both")) {
+ # ## convex hull stuff
+ # check.package("WhatIf")
+ # X <- model.matrix(reformulate(names(covs), intercept = FALSE), data = covs,
+ # contrasts.arg = lapply(Filter(is.factor, covs),
+ # function(x) contrasts(x, contrasts = nlevels(x) == 1)))
+ # discarded <- rep(FALSE, n.obs)
+ # if (option == "hull.control"){ # discard units not in T convex hull
+ # wif <- WhatIf::whatif(cfact = X[treat==0,], data = X[treat==1,])
+ # discarded[treat==0] <- !wif$in.hull
+ # } else if (option == "hull.treat") {
+ # wif <- WhatIf::whatif(cfact = X[treat==1,], data = X[treat==0,])
+ # discarded[treat==1] <- !wif$in.hull
+ # } else if (option == "hull.both"){ # discard units not in T&C convex hull
+ # wif <- WhatIf::whatif(cfact = cbind(1-treat, X), data = cbind(treat, X))
+ # discarded <- !wif$in.hull
+ # }
+ # }
- return(discarded)
+ setNames(discarded, names(treat))
}
diff --git a/R/dist_functions.R b/R/dist_functions.R
index bdbc6be..98ab444 100644
--- a/R/dist_functions.R
+++ b/R/dist_functions.R
@@ -7,7 +7,7 @@
#' splitting variable (i.e., the distances between all units in one group and
#' all units in the other). These distance matrices include the Mahalanobis
#' distance, Euclidean distance, scaled Euclidean distance, and robust
-#' (rank-based) Mahalanobs distance. These functions can be used as inputs to
+#' (rank-based) Mahalanobis distance. These functions can be used as inputs to
#' the `distance` argument to [matchit()] and are used to compute the
#' corresponding distance matrices within `matchit()` when named.
#'
@@ -41,7 +41,7 @@
#' @return A numeric distance matrix. When `formula` has a left-hand-side
#' (treatment) variable, the matrix will have one row for each treated unit and
#' one column for each control unit. Otherwise, the matrix will have one row
-#' and one column for each treated unit.
+#' and one column for each unit.
#'
#' @details
#' The **Euclidean distance** (computed using `euclidean_dist()`) is
@@ -215,7 +215,7 @@ transform_covariates <- function(formula = NULL, data = NULL, method = "mahalano
discarded = NULL) {
X <- get.covs.matrix.for.dist(formula, data)
- X <- check_X(X)
+ X <- .check_X(X)
treat <- check_treat(treat, X)
#If allvariables have no variance, use Euclidean to avoid errors
@@ -250,7 +250,7 @@ transform_covariates <- function(formula = NULL, data = NULL, method = "mahalano
}
}
else if (!is.cov_like(var)) {
- stop("If 'var' is not NULL, it must be a covariance matrix with as many entries as supplied variables.", call. = FALSE)
+ .err("if `var` is not `NULL`, it must be a covariance matrix with as many entries as supplied variables")
}
inv_var <- NULL
@@ -278,13 +278,13 @@ transform_covariates <- function(formula = NULL, data = NULL, method = "mahalano
var_r <- var_r * outer(multiplier, multiplier, "*")
inv_var <- NULL
- d <- det(var)
+ d <- det(var_r)
if (d > 1e-8) {
- inv_var <- try(solve(var), silent = TRUE)
+ inv_var <- try(solve(var_r), silent = TRUE)
}
if (d <= 1e-8 || inherits(inv_var, "try-error")) {
- inv_var <- generalized_inverse(var)
+ inv_var <- generalized_inverse(var_r)
}
if (any(discarded)) {
@@ -313,7 +313,7 @@ transform_covariates <- function(formula = NULL, data = NULL, method = "mahalano
sds <- sqrt(var)
}
else {
- stop("If 'var' is not NULL, it must be a covariance matrix or a vector of variances with as many entries as supplied variables.", call. = FALSE)
+ .err("if `var` is not `NULL`, it must be a covariance matrix or a vector of variances with as many entries as supplied variables")
}
for (i in seq_len(ncol(X))) {
@@ -345,7 +345,8 @@ eucdist_internal <- function(X, treat = NULL) {
d <- abs(outer(X[treat_l,], X[!treat_l,], "-"))
}
else {
- d <- dist_to_matrixC(dist(X))[treat_l, !treat_l, drop = FALSE]
+ d <- dist(X)
+ d <- dist_to_matrixC(d)[treat_l, !treat_l, drop = FALSE]
}
dimnames(d) <- list(rownames(X)[treat_l], rownames(X)[!treat_l])
}
@@ -360,13 +361,21 @@ get.covs.matrix.for.dist <- function(formula = NULL, data = NULL) {
if (is.null(formula)) {
if (is.null(colnames(data))) colnames(data) <- paste0("X", seq_len(ncol(data)))
fnames <- colnames(data)
- fnames[!startsWith(fnames, "`")] <- paste0("`", fnames[!startsWith(fnames, "`")], "`")
+ fnames[!startsWith(fnames, "`")] <- add_quotes(fnames[!startsWith(fnames, "`")], "`")
data <- as.data.frame(data)
- formula <- terms(reformulate(fnames), data = data)
+ formula <- reformulate(fnames)
}
else {
data <- as.data.frame(data)
- formula <- terms(formula, data = data)
+ }
+
+ formula <- terms(formula, data = data)
+
+ if (rlang::is_formula(formula, lhs = FALSE)) {
+ formula <- update(formula, ~ . + 1)
+ }
+ else {
+ formula <- update(formula, . ~ . + 1)
}
mf <- model.frame(formula, data, na.action = na.pass)
@@ -374,8 +383,6 @@ get.covs.matrix.for.dist <- function(formula = NULL, data = NULL) {
chars.in.mf <- vapply(mf, is.character, logical(1L))
mf[chars.in.mf] <- lapply(mf[chars.in.mf], factor)
- formula <- update(formula, NULL ~ . + 1)
-
X <- model.matrix(formula, data = mf,
contrasts.arg = lapply(Filter(is.factor, mf),
function(x) contrasts(x, contrasts = FALSE)/sqrt(2)))
@@ -388,9 +395,10 @@ get.covs.matrix.for.dist <- function(formula = NULL, data = NULL) {
attr(X, "treat") <- model.response(mf)
- return(X)
+ X
}
-check_X <- function(X) {
+
+.check_X <- function(X) {
if (isTRUE(attr(X, "checked"))) return(X)
treat <- attr(X, "treat")
@@ -401,8 +409,8 @@ check_X <- function(X) {
dimnames = list(names(X), NULL))
}
- if (anyNA(X)) stop("Missing values are not allowed in the covariates.", call. = FALSE)
- else if (any(!is.finite(X))) stop("Non-finite values are not allowed in the covariates.", call. = FALSE)
+ if (anyNA(X)) .err("missing values are not allowed in the covariates")
+ if (any(!is.finite(X))) .err("Non-finite values are not allowed in the covariates")
if (!is.numeric(X) || length(dim(X)) != 2) {
stop("bad X")
diff --git a/R/distance2_methods.R b/R/distance2_methods.R
index 55a3376..fd58d9d 100644
--- a/R/distance2_methods.R
+++ b/R/distance2_methods.R
@@ -304,12 +304,12 @@ distance2glm <- function(formula, data = NULL, link = "logit", ...) {
if (linear) pred <- predict(res, type = "link")
else pred <- predict(res, type = "response")
- return(list(model = res, distance = pred))
+ list(model = res, distance = pred)
}
#distance2gam-----------------
distance2gam <- function(formula, data = NULL, link = "logit", ...) {
- check.package("mgcv")
+ rlang::check_installed("mgcv")
linear <- !is.null(link) && startsWith(as.character(link), "linear")
if (linear) link <- sub("linear.", "", as.character(link), fixed = TRUE)
@@ -325,12 +325,12 @@ distance2gam <- function(formula, data = NULL, link = "logit", ...) {
if (linear) pred <- predict(res, type = "link")
else pred <- predict(res, type = "response")
- return(list(model = res, distance = as.numeric(pred)))
+ list(model = res, distance = as.numeric(pred))
}
#distance2rpart-----------------
distance2rpart <- function(formula, data = NULL, link = NULL, ...) {
- check.package("rpart")
+ rlang::check_installed("rpart")
A <- list(...)
A[!names(A) %in% c(names(formals(rpart::rpart)), names(formals(rpart::rpart.control)))] <- NULL
A$formula <- formula
@@ -338,24 +338,24 @@ distance2rpart <- function(formula, data = NULL, link = NULL, ...) {
A$method <- "class"
res <- do.call(rpart::rpart, A)
- return(list(model = res, distance = predict(res, type = "prob")[,"1"]))
+ list(model = res, distance = predict(res, type = "prob")[,"1"])
}
#distance2nnet-----------------
distance2nnet <- function(formula, data = NULL, link = NULL, ...) {
- check.package("nnet")
+ rlang::check_installed("nnet")
A <- list(...)
weights <- A$weights
A$weights <- NULL
res <- do.call(nnet::nnet, c(list(formula, data, weights = weights, entropy = TRUE), A), quote = TRUE)
- return(list(model = res, distance = drop(fitted(res))))
+ list(model = res, distance = drop(fitted(res)))
}
#distance2cbps-----------------
distance2cbps <- function(formula, data = NULL, link = NULL, ...) {
- check.package("CBPS")
+ rlang::check_installed("CBPS")
linear <- !is.null(link) && startsWith(as.character(link), "linear")
@@ -387,12 +387,12 @@ distance2cbps <- function(formula, data = NULL, link = NULL, ...) {
pred <- fitted(res)
if (linear) pred <- qlogis(pred)
- return(list(model = res, distance = pred))
+ list(model = res, distance = pred)
}
#distance2bart----------------
distance2bart <- function(formula, data = NULL, link = NULL, ...) {
- check.package("dbarts")
+ rlang::check_installed("dbarts")
linear <- !is.null(link) && startsWith(as.character(link), "linear")
@@ -406,11 +406,11 @@ distance2bart <- function(formula, data = NULL, link = NULL, ...) {
if (linear) pred <- fitted(res, type = "link")
else pred <- fitted(res, type = "response")
- return(list(model = res, distance = pred))
+ list(model = res, distance = pred)
}
# distance2bart <- function(formula, data, link = NULL, ...) {
-# check.package("BART")
+# rlang::check_installed("BART")
#
# if (!is.null(link) && startsWith(as.character(link), "linear")) {
# linear <- TRUE
@@ -453,17 +453,18 @@ distance2bart <- function(formula, data = NULL, link = NULL, ...) {
#distance2randomforest-----------------
distance2randomforest <- function(formula, data = NULL, link = NULL, ...) {
- check.package("randomForest")
+ rlang::check_installed("randomForest")
newdata <- get_all_vars(formula, data)
treatvar <- as.character(formula[[2]])
newdata[[treatvar]] <- factor(newdata[[treatvar]], levels = c("0", "1"))
res <- randomForest::randomForest(formula, data = newdata, ...)
- return(list(model = res, distance = predict(res, type = "prob")[,"1"]))
+
+ list(model = res, distance = predict(res, type = "prob")[,"1"])
}
#distance2glmnet--------------
distance2elasticnet <- function(formula, data = NULL, link = NULL, ...) {
- check.package("glmnet")
+ rlang::check_installed("glmnet")
linear <- !is.null(link) && startsWith(as.character(link), "linear")
if (linear) link <- sub("linear.", "", as.character(link), fixed = TRUE)
@@ -491,7 +492,7 @@ distance2elasticnet <- function(formula, data = NULL, link = NULL, ...) {
pred <- drop(predict(res, newx = A$x, s = s,
type = if (linear) "link" else "response"))
- return(list(model = res, distance = pred))
+ list(model = res, distance = pred)
}
distance2lasso <- function(formula, data = NULL, link = NULL, ...) {
A <- list(...)
@@ -508,7 +509,7 @@ distance2ridge <- function(formula, data = NULL, link = NULL, ...) {
#distance2gbm--------------
distance2gbm <- function(formula, data = NULL, link = NULL, ...) {
- check.package("gbm")
+ rlang::check_installed("gbm")
linear <- !is.null(link) && startsWith(as.character(link), "linear")
@@ -529,15 +530,14 @@ distance2gbm <- function(formula, data = NULL, link = NULL, ...) {
if (is.null(A[["keep.data"]])) A[["keep.data"]] <- FALSE
if (A[["cv.folds"]] <= 1 && A[["bag.fraction"]] == 1) {
- stop("Either `bag.fraction` must be less than 1 or `cv.folds` must be greater than 1 when using `distance = \"gbm\"`.",
- call. = FALSE)
+ .err('either `bag.fraction` must be less than 1 or `cv.folds` must be greater than 1 when using `distance = "gbm"`')
}
if (is.null(method)) {
if (A[["bag.fraction"]] < 1) method <- "OOB"
else method <- "cv"
}
else if (!tolower(method) %in% c("oob", "cv")) {
- stop("`distance.options$method` should be one of \"OOB\" or \"cv\".", call. = FALSE)
+ .err('`distance.options$method` should be one of "OOB" or "cv"')
}
res <- do.call(gbm::gbm, A)
@@ -547,5 +547,5 @@ distance2gbm <- function(formula, data = NULL, link = NULL, ...) {
pred <- drop(predict(res, newdata = data, n.trees = best.tree,
type = if (linear) "link" else "response"))
- return(list(model = res, distance = pred))
+ list(model = res, distance = pred)
}
diff --git a/R/get_weights_from_mm.R b/R/get_weights_from_mm.R
new file mode 100644
index 0000000..a7713d0
--- /dev/null
+++ b/R/get_weights_from_mm.R
@@ -0,0 +1,15 @@
+get_weights_from_mm <- function(match.matrix, treat) {
+
+ if (!is.integer(match.matrix)) match.matrix <- charmm2nummm(match.matrix, treat)
+
+ weights <- weights_matrixC(match.matrix, treat)
+
+ if (sum(weights) == 0)
+ .err("No units were matched")
+ if (sum(weights[treat == 1]) == 0)
+ .err("No treated units were matched")
+ if (sum(weights[treat == 0]) == 0)
+ .err("No control units were matched")
+
+ setNames(weights, names(treat))
+}
diff --git a/R/weights.subclass.R b/R/get_weights_from_subclass.R
similarity index 64%
rename from R/weights.subclass.R
rename to R/get_weights_from_subclass.R
index 4eea98a..b6e2baa 100644
--- a/R/weights.subclass.R
+++ b/R/get_weights_from_subclass.R
@@ -1,4 +1,4 @@
-weights.subclass <- function(psclass, treat, estimand = "ATT") {
+get_weights_from_subclass <- function(psclass, treat, estimand = "ATT") {
NAsub <- is.na(psclass)
@@ -24,30 +24,30 @@ weights.subclass <- function(psclass, treat, estimand = "ATT") {
weights[i0] <- (treated_by_sub/control_by_sub)[psclass[i0]]
#Weights average 1
- weights[i0] <- weights[i0]*sum(i0)/sum(weights[i0])
+ weights[i0] <- .make_sum_to_n(weights[i0])
}
else if (estimand == "ATC") {
weights[i1] <- (control_by_sub/treated_by_sub)[psclass[i1]]
weights[i0] <- 1
#Weights average 1
- weights[i1] <- weights[i1]*sum(i1)/sum(weights[i1])
+ weights[i1] <- .make_sum_to_n(weights[i1])
}
else if (estimand == "ATE") {
weights[i1] <- (total_by_sub/treated_by_sub)[psclass[i1]]
weights[i0] <- (total_by_sub/control_by_sub)[psclass[i0]]
#Weights average 1
- weights[i1] <- weights[i1]*sum(i1)/sum(weights[i1])
- weights[i0] <- weights[i0]*sum(i0)/sum(weights[i0])
+ weights[i1] <- .make_sum_to_n(weights[i1])
+ weights[i0] <- .make_sum_to_n(weights[i0])
}
- if (sum(weights)==0)
- stop("No units were matched.", call. = FALSE)
- else if (sum(weights[treat == 1])==0)
- stop("No treated units were matched.", call. = FALSE)
- else if (sum(weights[treat == 0])==0)
- stop("No control units were matched.", call. = FALSE)
+ if (sum(weights) == 0)
+ .err("No units were matched")
+ if (sum(weights[treat == 1]) == 0)
+ .err("No treated units were matched")
+ if (sum(weights[treat == 0]) == 0)
+ .err("No control units were matched")
- return(weights)
+ weights
}
diff --git a/R/input_processing.R b/R/input_processing.R
index 6f78e11..305229d 100644
--- a/R/input_processing.R
+++ b/R/input_processing.R
@@ -120,18 +120,16 @@ check.inputs <- function(mcall, method, distance, exact, mahvars, antiexact,
}
}
- if (length(ignored.inputs) > 0) warning(sprintf("The %s %s not used with `method = %s` and will be ignored.",
+ if (length(ignored.inputs) > 0) .wrn(sprintf("the %s %s not used with `method = %s` and will be ignored",
ngettext(length(ignored.inputs), "argument", "arguments"),
word_list(ignored.inputs, quotes = 1, is.are = TRUE),
- add_quotes(method, quotes = !null.method)),
- call. = FALSE, immediate. = TRUE)
- if (length(error.inputs) > 0) stop(sprintf("The %s %s not used with `method = %s` and distance = \"%s\".",
+ add_quotes(method, quotes = !null.method)))
+ if (length(error.inputs) > 0) .err(sprintf("the %s %s not used with `method = %s` and `distance = \"%s\"`",
ngettext(length(error.inputs), "argument", "arguments"),
word_list(error.inputs, quotes = 1, is.are = TRUE),
add_quotes(method, quotes = !null.method),
- distance),
- call. = FALSE)
- return(ignored.inputs)
+ distance))
+ ignored.inputs
}
#Check treatment for type, binary, missing, num. rows
@@ -144,12 +142,12 @@ check_treat <- function(treat = NULL, X = NULL) {
if (isTRUE(attr(treat, "checked"))) return(treat)
if (!is.atomic(treat) || !is.null(dim(treat))) {
- stop("The treatment must be a vector.", call. = FALSE)
+ .err("the treatment must be a vector")
}
- if (anyNA(treat)) stop("Missing values are not allowed in the treatment.", call. = FALSE)
- if (length(unique(treat)) != 2) stop("The treatment must be a binary variable.", call. = FALSE)
- if (!is.null(X) && length(treat) != nrow(X)) stop("The treatment and covariates must have the same number of units.", call. = FALSE)
+ if (anyNA(treat)) .err("missing values are not allowed in the treatment")
+ if (length(unique(treat)) != 2) .err("the treatment must be a binary variable")
+ if (!is.null(X) && length(treat) != nrow(X)) .err("the treatment and covariates must have the same number of units")
treat <- binarize(treat) #make 0/1
attr(treat, "checked") <- TRUE
@@ -158,9 +156,12 @@ check_treat <- function(treat = NULL, X = NULL) {
#Function to process distance and give warnings about new syntax
process.distance <- function(distance, method = NULL, treat) {
- if (is.null(distance) && !is.null(method) %% !method %in% c("cem", "exact", "cardinality")) {
- stop(sprintf("`distance` cannot be `NULL` with `method = \"%s\"`.",
- method), call. = FALSE)
+ if (is.null(distance)) {
+ if (!is.null(method) && !method %in% c("cem", "exact", "cardinality")) {
+ .err(sprintf("`distance` cannot be `NULL` with `method = \"%s\"`",
+ method))
+ }
+ return(distance)
}
if (is.character(distance) && length(distance) == 1) {
@@ -175,15 +176,15 @@ process.distance <- function(distance, method = NULL, treat) {
if (tolower(distance) %in% c("cauchit", "cloglog", "linear.cloglog", "linear.log", "linear.logit", "linear.probit",
"linear.cauchit", "log", "probit")) {
link <- tolower(distance)
- warning(sprintf("`distance = \"%s\"` will be deprecated; please use `distance = \"glm\", link = \"%s\"` in the future.",
- distance, link), call. = FALSE, immediate. = TRUE)
+ .wrn(sprintf("`distance = \"%s\"` will be deprecated; please use `distance = \"glm\", link = \"%s\"` in the future",
+ distance, link))
distance <- "glm"
attr(distance, "link") <- link
}
else if (tolower(distance) %in% tolower(c("GAMcloglog", "GAMlog", "GAMlogit", "GAMprobit"))) {
link <- tolower(substr(distance, 4, nchar(distance)))
- warning(sprintf("`distance = \"%s\"` will be deprecated; please use `distance = \"gam\", link = \"%s\"` in the future.",
- distance, link), call. = FALSE, immediate. = TRUE)
+ .wrn(sprintf("`distance = \"%s\"` will be deprecated; please use `distance = \"gam\", link = \"%s\"` in the future",
+ distance, link))
distance <- "gam"
attr(distance, "link") <- link
}
@@ -195,11 +196,11 @@ process.distance <- function(distance, method = NULL, treat) {
distance <- "elasticnet"
}
else if (!tolower(distance) %in% allowable.distances) {
- stop("The argument supplied to `distance` is not an allowable value. See `help(\"distance\")` for allowable options.", call. = FALSE)
+ .err("the argument supplied to `distance` is not an allowable value. See `help(\"distance\")` for allowable options")
}
else if (!is.null(method) && method == "subclass" && tolower(distance) %in% matchit_distances()) {
- stop(sprintf("`distance` cannot be %s with `method = \"subclass\"`.",
- add_quotes(distance)), call. = FALSE)
+ .err(sprintf("`distance` cannot be %s with `method = \"subclass\"`",
+ add_quotes(distance)))
}
else {
distance <- tolower(distance)
@@ -207,11 +208,11 @@ process.distance <- function(distance, method = NULL, treat) {
}
else if (!is.numeric(distance) || (!is.null(dim(distance)) && length(dim(distance)) != 2)) {
- stop("`distance` must be a string with the name of the distance measure to be used or a numeric vector or matrix containing distance measures.", call. = FALSE)
+ .err("`distance` must be a string with the name of the distance measure to be used or a numeric vector or matrix containing distance measures")
}
else if (is.matrix(distance) && (is.null(method) || !method %in% c("nearest", "optimal", "full"))) {
- stop(sprintf("`distance` cannot be supplied as a matrix with `method = %s`.",
- add_quotes(method, quotes = !is.null(method))), call. = FALSE)
+ .err(sprintf("`distance` cannot be supplied as a matrix with `method = %s`",
+ add_quotes(method, quotes = !is.null(method))))
}
if (is.numeric(distance)) {
@@ -227,20 +228,19 @@ process.distance <- function(distance, method = NULL, treat) {
if (!is.null(colnames(distance))) distance <- distance[,names(treat)[treat == 0], drop = FALSE]
}
else {
- stop("When supplied as a matrix, `distance` must have dimensions NxN or N1xN0. See `help(\"distance\")` for details.", call. = FALSE)
+ .err("when supplied as a matrix, `distance` must have dimensions NxN or N1xN0. See `help(\"distance\")` for details")
}
}
else {
if (length(distance) != length(treat)) {
- stop("`distance` must be the same length as the dataset if specified as a numeric vector.", call. = FALSE)
+ .err("`distance` must be the same length as the dataset if specified as a numeric vector")
}
}
- if (anyNA(distance)) {
- stop("Missing values are not allowed in `distance`.", call. = FALSE)
- }
+ chk::chk_not_any_na(distance)
}
- return(distance)
+
+ distance
}
#Function to check ratio is acceptable
@@ -250,58 +250,57 @@ process.ratio <- function(ratio, method = NULL, ..., min.controls = NULL, max.co
ratio.na <- !ratio.null && anyNA(ratio)
if (is.null(method)) return(1)
- else if (method %in% c("nearest", "optimal")) {
+ if (method %in% c("nearest", "optimal")) {
if (ratio.null) ratio <- 1
- else if (ratio.na) stop("`ratio` cannot be NA.", call. = FALSE)
+ else if (ratio.na) .err("`ratio` cannot be `NA`")
else if (!is.atomic(ratio) || !is.numeric(ratio) || length(ratio) > 1 || ratio < 1) {
- stop("`ratio` must be a single number greater than or equal to 1.", call. = FALSE)
+ .err("`ratio` must be a single number greater than or equal to 1")
}
if (is.null(max.controls)) {
- if (!is_whole_number(ratio)) {
- stop("`ratio` must be a whole number when `max.controls` is not specified.",
- call. = FALSE)
+ if (!chk::vld_whole_number(ratio)) {
+ .err("`ratio` must be a whole number when `max.controls` is not specified")
}
ratio <- round(ratio)
}
else if (anyNA(max.controls) || !is.atomic(max.controls) || !is.numeric(max.controls) || length(max.controls) > 1) {
- stop("`max.controls` must be a single positive number.", call. = FALSE)
+ .err("`max.controls` must be a single positive number")
}
else {
- if (ratio <= 1) stop("`ratio` must be greater than 1 for variable ratio matching.", call. = FALSE)
+ if (ratio <= 1) .err("`ratio` must be greater than 1 for variable ratio matching")
max.controls <- ceiling(max.controls)
- if (max.controls <= ratio) stop("`max.controls` must be greater than `ratio` for variable ratio matching.", call. = FALSE)
+ if (max.controls <= ratio) .err("`max.controls` must be greater than `ratio` for variable ratio matching")
if (is.null(min.controls)) min.controls <- 1
else if (anyNA(max.controls) || !is.atomic(max.controls) || !is.numeric(max.controls) || length(max.controls) > 1) {
- stop("`max.controls` must be a single positive number.", call. = FALSE)
+ .err("`max.controls` must be a single positive number")
}
else min.controls <- floor(min.controls)
- if (min.controls < 1) stop("`min.controls` cannot be less than 1 for variable ratio matching.", call. = FALSE)
- else if (min.controls >= ratio) stop("`min.controls` must be less than `ratio` for variable ratio matching.", call. = FALSE)
+ if (min.controls < 1) .err("`min.controls` cannot be less than 1 for variable ratio matching")
+ if (min.controls >= ratio) .err("`min.controls` must be less than `ratio` for variable ratio matching")
}
}
else if (method == "full") {
if (is.null(max.controls)) max.controls <- Inf
else if ((anyNA(max.controls) || !is.atomic(max.controls) || !is.numeric(max.controls) || length(max.controls) > 1)) {
- stop("`max.controls` must be a single positive number.", call. = FALSE)
+ .err("`max.controls` must be a single positive number")
}
if (is.null(min.controls)) min.controls <- 0
else if ((anyNA(min.controls) || !is.atomic(min.controls) || !is.numeric(min.controls) || length(min.controls) > 1)) {
- stop("`min.controls` must be a single positive number.", call. = FALSE)
+ .err("`min.controls` must be a single positive number")
}
ratio <- 1 #Just to get min.controls and max.controls out
}
else if (method == "genetic") {
if (ratio.null) ratio <- 1
- else if (ratio.na) stop("`ratio` cannot be NA.", call. = FALSE)
+ else if (ratio.na) .err("`ratio` cannot be `NA`")
else if (!is.atomic(ratio) || !is.numeric(ratio) || length(ratio) > 1 || ratio < 1 ||
- !is_whole_number(ratio)) {
- stop("`ratio` must be a single whole number greater than or equal to 1.", call. = FALSE)
+ !chk::vld_whole_number(ratio)) {
+ .err("`ratio` must be a single whole number greater than or equal to 1")
}
ratio <- round(ratio)
@@ -310,7 +309,7 @@ process.ratio <- function(ratio, method = NULL, ..., min.controls = NULL, max.co
else if (method == "cardinality") {
if (ratio.null) ratio <- 1
else if (!ratio.na && (!is.atomic(ratio) || !is.numeric(ratio) || length(ratio) > 1 || ratio < 0)) {
- stop("`ratio` must be a single positive number or NA.", call. = FALSE)
+ .err("`ratio` must be a single positive number or `NA`")
}
min.controls <- max.controls <- NULL
@@ -323,7 +322,8 @@ process.ratio <- function(ratio, method = NULL, ..., min.controls = NULL, max.co
attr(ratio, "min.controls") <- min.controls
attr(ratio, "max.controls") <- max.controls
}
- return(ratio)
+
+ ratio
}
#Function to check if caliper is okay and process it
@@ -342,34 +342,42 @@ process.caliper <- function(caliper = NULL, method = NULL, data = NULL, covs = N
if (length(caliper) == 0 || is.null(method) || !method %in% c("nearest", "genetic", "full", "quick")) return(NULL)
#Check if form of caliper is okay
- if (!is.atomic(caliper) || !is.numeric(caliper)) stop("`caliper` must be a numeric vector.", call. = FALSE)
+ if (!is.atomic(caliper) || !is.numeric(caliper)) .err("`caliper` must be a numeric vector")
#Check caliper names
- if (length(caliper) == 1 && (is.null(names(caliper)) || identical(names(caliper), ""))) names(caliper) <- ""
- else if (is.null(names(caliper))) stop("`caliper` must be a named vector with names corresponding to the variables for which a caliper is to be applied.", call. = FALSE)
- else if (anyNA(names(caliper))) stop("`caliper` names cannot include NA.", call. = FALSE)
- else if (sum(names(caliper) == "") > 1) stop("No more than one entry in `caliper` can have no name.", call. = FALSE)
+ if (length(caliper) == 1 && (is.null(names(caliper)) || identical(names(caliper), ""))) {
+ names(caliper) <- ""
+ }
+ else if (is.null(names(caliper))) {
+ .err("`caliper` must be a named vector with names corresponding to the variables for which a caliper is to be applied")
+ }
+ else if (anyNA(names(caliper))) {
+ .err("`caliper` names cannot include `NA`")
+ }
+ else if (sum(names(caliper) == "") > 1) {
+ .err("no more than one entry in `caliper` can have no name")
+ }
- if (any(names(caliper) == "") && is.null(distance)) stop("All entries in `caliper` must be named when distance does not correspond to a propensity score.", call. = FALSE)
+ if (any(names(caliper) == "") && is.null(distance)) {
+ .err("all entries in `caliper` must be named when `distance` does not correspond to a propensity score")
+ }
#Check if caliper name is in available data
cal.in.data <- setNames(names(caliper) %in% names(data), names(caliper))
cal.in.covs <- setNames(names(caliper) %in% names(covs), names(caliper))
cal.in.mahcovs <- setNames(names(caliper) %in% names(mahcovs), names(caliper))
if (any(names(caliper) != "" & !cal.in.covs & !cal.in.data)) {
- stop(paste0("All variables named in `caliper` must be in `data`. Variables not in `data`:\n\t",
- paste0(names(caliper)[names(caliper) != "" & !cal.in.data & !cal.in.covs & !cal.in.mahcovs], collapse = ", ")), call. = FALSE)
+ .err(paste0("All variables named in `caliper` must be in `data`. Variables not in `data`:\n\t",
+ paste0(names(caliper)[names(caliper) != "" & !cal.in.data & !cal.in.covs & !cal.in.mahcovs], collapse = ", ")), tidy = FALSE)
}
#Check std.caliper
- if (length(std.caliper) == 0 || !is.atomic(std.caliper) || !is.logical(std.caliper)) {
- stop("`std.caliper` must be a logical (TRUE/FALSE) vector.", call. = FALSE)
- }
+ chk::chk_logical(std.caliper)
if (length(std.caliper) == 1) {
std.caliper <- setNames(rep.int(std.caliper, length(caliper)), names(caliper))
}
else if (length(std.caliper) != length(caliper)) {
- stop("`std.caliper` must be the same length as `caliper`", call. = FALSE)
+ .err("`std.caliper` must be the same length as `caliper`")
}
else names(std.caliper) <- names(caliper)
@@ -392,28 +400,28 @@ process.caliper <- function(caliper = NULL, method = NULL, data = NULL, covs = N
#Ensure no calipers on categorical variables
cat.vars <- vapply(names(caliper), function(x) {
if (num.unique[names(num.unique) == x] == 2) return(TRUE)
- else {
- if (x == "") var <- distance
- else if (cal.in.data[x]) var <- data[[x]]
- else if (cal.in.covs[x]) var <- covs[[x]]
- else var <- mahcovs[[x]]
- return(is.factor(var) || is.character(var))
- }
+ if (x == "") var <- distance
+ else if (cal.in.data[x]) var <- data[[x]]
+ else if (cal.in.covs[x]) var <- covs[[x]]
+ else var <- mahcovs[[x]]
+
+ is.factor(var) || is.character(var)
}, logical(1L))
if (any(cat.vars)) {
- stop(paste0("Calipers cannot be used with binary, factor, or character variables. Offending variables:\n\t",
- paste0(ifelse(names(caliper) == "", "<distance>", names(caliper))[cat.vars], collapse = ", ")), call. = FALSE)
+ .err(paste0("Calipers cannot be used with binary, factor, or character variables. Offending variables:\n\t",
+ paste0(ifelse(names(caliper) == "", "<distance>", names(caliper))[cat.vars], collapse = ", ")),
+ tidy = FALSE)
}
#Process calipers according to std.caliper
std.caliper <- std.caliper[names(std.caliper) %in% names(caliper)]
- if (anyNA(std.caliper)) stop("`std.caliper` cannot be NA.", call. = FALSE)
+ chk::chk_not_any_na(std.caliper)
if (any(std.caliper)) {
if ("" %in% names(std.caliper) && isTRUE(std.caliper[names(std.caliper) == ""]) && is.matrix(distance)) {
- stop("When `distance` is supplied as a matrix and a caliper for it is specified, `std.caliper` must be FALSE for the distance measure.", call. = FALSE)
+ .err("when `distance` is supplied as a matrix and a caliper for it is specified, `std.caliper` must be `FALSE` for the distance measure")
}
caliper[std.caliper] <- caliper[std.caliper] * vapply(names(caliper)[std.caliper], function(x) {
if (x == "") sd(distance[!discarded])
@@ -428,8 +436,7 @@ process.caliper <- function(caliper = NULL, method = NULL, data = NULL, covs = N
attr(caliper, "cal.formula") <- reformulate(names(caliper)[names(caliper) != "" & !cal.in.covs[names(caliper)] & !cal.in.mahcovs[names(caliper)]])
}
- return(abs(caliper))
-
+ abs(caliper)
}
#Function to process replace argument
@@ -438,9 +445,7 @@ process.replace <- function(replace, method = NULL, ..., reuse.max = NULL) {
if (is.null(method)) return(FALSE)
if (is.null(replace)) replace <- FALSE
- else if (anyNA(replace) || length(replace) != 1 || !is.logical(replace)) {
- stop("`replace` must be TRUE or FALSE.", call. = FALSE)
- }
+ chk::chk_flag(replace)
if (method %in% c("nearest")) {
if (is.null(reuse.max)) {
@@ -454,7 +459,7 @@ process.replace <- function(replace, method = NULL, ..., reuse.max = NULL) {
}
else if (abs(reuse.max - round(reuse.max)) > 1e-8 || length(reuse.max) != 1 ||
anyNA(reuse.max) || reuse.max < 1) {
- stop("`reuse.max` must be a positive integer of length 1.", call. = FALSE)
+ .err("`reuse.max` must be a positive integer of length 1")
}
replace <- reuse.max != 1L
@@ -465,6 +470,7 @@ process.replace <- function(replace, method = NULL, ..., reuse.max = NULL) {
}
#Process variable input, e.g., to exact or mahvars, that accept a string or rhs formula
+#Returns a model.frame object
process.variable.input <- function(x, data = NULL) {
n <- deparse1(substitute(x))
@@ -472,23 +478,26 @@ process.variable.input <- function(x, data = NULL) {
if (is.character(x)) {
if (is.null(data) || !is.data.frame(data)) {
- stop(sprintf("If `%s` is specified as strings, a data frame containing the named variables must be supplied to `data`.",
- n), call. = FALSE)
+ .err(sprintf("if `%s` is specified as strings, a data frame containing the named variables must be supplied to `data`",
+ n))
}
if (!all(x %in% names(data))) {
- stop(sprintf("All names supplied to `%s` must be variables in `data`. Variables not in `data`:\n\t%s", n,
- paste(add_quotes(setdiff(x, names(data))), collapse = ", ")), call. = FALSE)
+ .err(sprintf("All names supplied to `%s` must be variables in `data`. Variables not in `data`:\n\t%s", n,
+ paste(add_quotes(setdiff(x, names(data))), collapse = ", ")), tidy = FALSE)
}
x <- reformulate(x)
}
- else if (inherits(x, "formula")) {
- x <- update(x, NULL ~ .)
+ else if (rlang::is_formula(x)) {
+ x <- update(terms(x, data = data), NULL ~ .)
}
else {
- stop(sprintf("`%s` must be supplied as a character vector of names or a one-sided formula.", n), call. = FALSE)
+ .err(sprintf("`%s` must be supplied as a character vector of names or a one-sided formula.", n))
}
+
x_covs <- model.frame(x, data, na.action = "na.pass")
- if (anyNA(x_covs)) stop(sprintf("Missing values are not allowed in the covariates named in `%s`.", n), call. = FALSE)
+ if (anyNA(x_covs)) {
+ .err(sprintf("missing values are not allowed in the covariates named in `%s`", n))
+ }
x_covs
}
\ No newline at end of file
diff --git a/R/match.data.R b/R/match.data.R
index 6e69e63..ce53841 100644
--- a/R/match.data.R
+++ b/R/match.data.R
@@ -57,7 +57,7 @@
#' to be used as the dataset input in calls to `glm()` or similar to
#' estimate treatment effects in the matched sample. It is important to include
#' the weights in the estimation of the effect and its standard error. The
-#' subclass column, when created, contains par or subclass membership and
+#' subclass column, when created, contains pair or subclass membership and
#' should be used to estimate the effect and its standard error. Subclasses
#' will only be included if there is a `subclass` component in the
#' `matchit` object, which does not occur with matching with replacement,
@@ -72,8 +72,8 @@
#' each pair they are a part of. For example, if matching was performed with
#' replacement and a control unit was matched to two treated units, that
#' control unit will have two rows in the output dataset, one for each pair it
-#' is a part of. Weights are computed for each row, and are equal to the
-#' inverse of the number of control units in each control unit's subclass.
+#' is a part of. Weights are computed for each row, and, for control units, are equal to the
+#' inverse of the number of control units in each control unit's subclass; treated units get a weight of 1.
#' Unmatched units are dropped. An additional column with unit IDs will be
#' created (named using the `id` argument) to identify when the same unit
#' is present in multiple rows. This dataset structure allows for the inclusion
@@ -87,8 +87,8 @@
#' replacement.
#'
#' @return
-#' A data frame containing the data supplied in the `data`
-#' argument or in the original call to `matchit()` with the computed
+#' A data frame containing the data supplied in the `data` argument or in the
+#' original call to `matchit()` with the computed
#' output variables appended as additional columns, named according the
#' arguments above. For `match.data()`, the `group` and
#' `drop.unmatched` arguments control whether only subsets of the data are
@@ -176,9 +176,7 @@
match.data <- function(object, group = "all", distance = "distance", weights = "weights", subclass = "subclass",
data = NULL, include.s.weights = TRUE, drop.unmatched = TRUE) {
- if (!inherits(object, "matchit")) {
- stop("'object' must be a matchit object, the output of a call to matchit().", call. = FALSE)
- }
+ chk::chk_is(object, "matchit")
if (is.null(data)) {
env <- environment(object$formula)
@@ -189,7 +187,7 @@ match.data <- function(object, group = "all", distance = "distance", weights = "
if (length(data) == 0 || inherits(data, "try-error") || length(dim(data)) != 2 || nrow(data) != length(object[["treat"]])) {
data <- object[["model"]][["data"]]
if (length(data) == 0 || nrow(data) != length(object[["treat"]])) {
- stop("A valid dataset could not be found. Please supply an argument to 'data' containing the original dataset used in the matching.", call. = FALSE)
+ .err("a valid dataset could not be found. Please supply an argument to `data` containing the original dataset used in the matching")
}
}
}
@@ -197,30 +195,28 @@ match.data <- function(object, group = "all", distance = "distance", weights = "
if (!is.data.frame(data)) {
if (is.matrix(data)) data <- as.data.frame.matrix(data)
- else stop("'data' must be a data frame.", call. = FALSE)
+ else .err("`data` must be a data frame")
}
if (nrow(data) != length(object$treat)) {
- stop("'data' must have as many rows as there were units in the original call to matchit().", call. = FALSE)
+ .err("`data` must have as many rows as there were units in the original call to `matchit()`")
}
if (!is.null(object$distance)) {
- if (is.null(distance)) stop("The argument to 'distance' cannot be NULL.", call. = FALSE)
- if (!is.atomic(distance) || !is.character(distance) || length(distance) != 1 || is.na(distance)) {
- stop("The argument to 'distance' must be a string of length 1.", call. = FALSE)
- }
+ chk::chk_not_null(distance)
+ chk::chk_string(distance)
if (distance %in% names(data)) {
- stop(paste0("\"", distance, "\" is already the name of a variable in the data. Please choose another name for distance using the 'distance' argument."), call. = FALSE)
+ .err(sprintf("%s is already the name of a variable in the data. Please choose another name for distance using the `distance` argument",
+ add_quotes(distance)))
}
data[[distance]] <- object$distance
}
if (!is.null(object$weights)) {
- if (is.null(weights)) stop("The argument to 'weights' cannot be NULL.", call. = FALSE)
- if (!is.atomic(weights) || !is.character(weights) || length(weights) != 1 || is.na(weights)) {
- stop("The argument to 'weights' must be a string of length 1.", call. = FALSE)
- }
+ chk::chk_not_null(weights)
+ chk::chk_string(weights)
if (weights %in% names(data)) {
- stop(paste0("\"", weights, "\" is already the name of a variable in the data. Please choose another name for weights using the 'weights' argument."), call. = FALSE)
+ .err(sprintf("%s is already the name of a variable in the data. Please choose another name for weights using the `weights` argument",
+ add_quotes(weights)))
}
data[[weights]] <- object$weights
@@ -230,12 +226,11 @@ match.data <- function(object, group = "all", distance = "distance", weights = "
}
if (!is.null(object$subclass)) {
- if (is.null(subclass)) stop("The argument to 'subclass' cannot be NULL.", call. = FALSE)
- if (!is.atomic(subclass) || !is.character(subclass) || length(subclass) != 1 || is.na(subclass)) {
- stop("The argument to 'subclass' must be a string of length 1.", call. = FALSE)
- }
+ chk::chk_not_null(subclass)
+ chk::chk_string(subclass)
if (subclass %in% names(data)) {
- stop(paste0("\"", subclass, "\" is already the name of a variable in the data. Please choose another name for subclass using the 'subclass' argument."), call. = FALSE)
+ .err(sprintf("%s is already the name of a variable in the data. Please choose another name for subclass using the `subclass` argument",
+ add_quotes(subclass)))
}
data[[subclass]] <- object$subclass
}
@@ -257,7 +252,7 @@ match.data <- function(object, group = "all", distance = "distance", weights = "
class(data) <- c("matchdata", class(data))
- return(data)
+ data
}
#' @export
@@ -265,12 +260,10 @@ match.data <- function(object, group = "all", distance = "distance", weights = "
get_matches <- function(object, distance = "distance", weights = "weights", subclass = "subclass",
id = "id", data = NULL, include.s.weights = TRUE) {
- if (!inherits(object, "matchit")) {
- stop("'object' must be a matchit object, the output of a call to matchit().", call. = FALSE)
- }
+ chk::chk_is(object, "matchit")
if (is.null(object$match.matrix)) {
- stop("A match.matrix component must be present in the matchit object, which does not occur with all types of matching. Use match.data() instead.", call. = FALSE)
+ .err("a match.matrix component must be present in the matchit object, which does not occur with all types of matching. Use `match.data()` instead")
}
#Get initial data using match.data; note weights and subclass will be removed,
@@ -279,13 +272,12 @@ get_matches <- function(object, distance = "distance", weights = "weights", subc
weights = weights, subclass = subclass, data = data,
include.s.weights = FALSE, drop.unmatched = TRUE)
- if (is.null(id)) stop("The argument to 'id' cannot be NULL.", call. = FALSE)
- if (!is.atomic(id) || !is.character(id) || length(id) != 1 || is.na(id)) {
- stop("The argument to 'id' must be a string of length 1.", call. = FALSE)
- }
+ chk::chk_not_null(id)
+ chk::chk_string(id)
if (id %in% names(m.data)) {
- stop(paste0("\"", id, "\" is already the name of a variable in the data. Please choose another name for id using the 'id' argument."), call. = FALSE)
+ .err(sprintf("%s is already the name of a variable in the data. Please choose another name for id using the `id` argument",
+ add_quotes(id)))
}
m.data[[id]] <- names(object$treat)[object$weights > 0]
@@ -325,5 +317,5 @@ get_matches <- function(object, distance = "distance", weights = "weights", subc
class(out) <- c("getmatches", class(out))
- return(out)
+ out
}
diff --git a/R/match.qoi.R b/R/match.qoi.R
index 3c1ec45..c4b4f01 100644
--- a/R/match.qoi.R
+++ b/R/match.qoi.R
@@ -181,17 +181,17 @@ qqsum <- function(x, t, w = NULL, standardize = FALSE) {
t1 <- t == t[1]
#For binary variables, just difference in means
ediff <- abs(wm(x[t1], w[t1]) - wm(x[-t1], w[-t1]))
- return(c(meandiff = ediff, meddiff = ediff, maxdiff = ediff))
+ return(c(meandiff = ediff, maxdiff = ediff))
}
- for (i in unique(t, nmax = 2)) w[t==i] <- w[t==i]/sum(w[t==i])
+ w <- .make_sum_to_1(w, by = t)
ord <- order(x)
x_ord <- x[ord]
w_ord <- w[ord]
t_ord <- t[ord]
- t1 <- which(t_ord==t_ord[1])
+ t1 <- which(t_ord == t_ord[1])
if (standardize) {
#Difference between ecdf of x for each group
@@ -205,39 +205,43 @@ qqsum <- function(x, t, w = NULL, standardize = FALSE) {
u <- unique(x_ord)
- wn1 <- sum(w[t1] > 0)
- wn0 <- sum(w[-t1] > 0)
-
w1 <- w_ord[t1]
w0 <- w_ord[-t1]
x1 <- x_ord[t1][w1 > 0]
x0 <- x_ord[-t1][w0 > 0]
+ w1 <- w1[w1 > 0]
+ w0 <- w0[w0 > 0]
+
+ wn1 <- length(w1)
+ wn0 <- length(w0)
+
if (wn1 < wn0) {
if (length(u) <= 5) {
- x0probs <- vapply(u, function(u_) wm(x0 == u_, w0[w0 > 0]), numeric(1L))
- x0cumprobs <- c(0, cumsum(x0probs)[-length(u)], 1)
- x0 <- u[findInterval(cumsum(w1[w1 > 0]), x0cumprobs, rightmost.closed = TRUE)]
+ x0probs <- vapply(u, function(u_) wm(x0 == u_, w0), numeric(1L))
+ x0cumprobs <- c(0, .cumsum_prob(x0probs))
+ x0 <- u[findInterval(.cumsum_prob(w1), x0cumprobs, rightmost.closed = TRUE)]
}
else {
- x0 <- approx(cumsum(w0[w0 > 0]), y = x0, xout = cumsum(w1[w1 > 0]), rule = 2,
+ x0 <- approx(.cumsum_prob(w0), y = x0, xout = .cumsum_prob(w1), rule = 2,
method = "constant", ties = "ordered")$y
}
}
- else {
+ else if (wn1 > wn0) {
if (length(u) <= 5) {
- x1probs <- vapply(u, function(u_) wm(x1 == u_, w1[w1 > 0]), numeric(1L))
- x1cumprobs <- c(0, cumsum(x1probs)[-length(u)], 1)
- x1 <- u[findInterval(cumsum(w0[w0 > 0]), x1cumprobs, rightmost.closed = TRUE)]
+ x1probs <- vapply(u, function(u_) wm(x1 == u_, w1), numeric(1L))
+ x1cumprobs <- c(0, .cumsum_prob(x1probs))
+ x1 <- u[findInterval(.cumsum_prob(w0), x1cumprobs, rightmost.closed = TRUE)]
}
else {
- x1 <- approx(cumsum(w1[w1 > 0]), y = x1, xout = cumsum(w0[w0 > 0]), rule = 2,
+ x1 <- approx(.cumsum_prob(w1), y = x1, xout = .cumsum_prob(w0), rule = 2,
method = "constant", ties = "ordered")$y
}
}
ediff <- abs(x1 - x0)
}
- return(c(meandiff = mean(ediff), maxdiff = max(ediff)))
+
+ c(meandiff = mean(ediff), maxdiff = max(ediff))
}
\ No newline at end of file
diff --git a/R/matchit.R b/R/matchit.R
index 3daa688..49c2b66 100644
--- a/R/matchit.R
+++ b/R/matchit.R
@@ -109,15 +109,7 @@
#' individual methods pages for information on whether and how this argument is
#' used. Default is `FALSE` for matching without replacement.
#' @param m.order for methods that allow it, the order that the matching takes
-#' place. Allowable options depend on the matching method but include
-#' `"largest"`, where matching takes place in descending order of distance
-#' measures; `"smallest"`, where matching takes place in ascending order
-#' of distance measures; `"random"`, where matching takes place in a
-#' random order; and `"data"` where matching takes place based on the
-#' order of units in the data. When `m.order = "random"`, results may
-#' differ across different runs of the same code unless a seed is set and
-#' specified with [set.seed()]. See the individual methods pages for
-#' information on whether and how this argument is used. The default of
+#' place. Allowable options depend on the matching method. The default of
#' `NULL` corresponds to `"largest"` when a propensity score is
#' estimated or supplied as a vector and `"data"` otherwise.
#' @param caliper for methods that allow it, the width(s) of the caliper(s) to
@@ -230,14 +222,14 @@
#' (in the case of k:1 matching) or the stratum they belong to (in the case of
#' exact matching, coarsened exact matching, full matching, or
#' subclassification). The formula for computing the weights depends on the
-#' argument supplied to `estimand`. A new stratum "propensity score"
-#' (`p`) is computed as the proportion of units in each stratum that are
+#' argument supplied to `estimand`. A new "stratum propensity score"
+#' (`sp`) is computed as the proportion of units in each stratum that are
#' in the treated group, and all units in that stratum are assigned that
-#' propensity score. Weights are then computed using the standard formulas for
-#' inverse probability weights: for the ATT, weights are 1 for the treated
-#' units and `p/(1-p)` for the control units; for the ATC, weights are
-#' `(1-p)/p` for the treated units and 1 for the control units; for the
-#' ATE, weights are `1/p` for the treated units and `1/(1-p)` for the
+#' stratum propensity score. This is distinct from the propensity score used for matching, if any. Weights are then computed using the standard formulas for
+#' inverse probability weights with the stratum propensity score inserted: for the ATT, weights are 1 for the treated
+#' units and `sp/(1-sp)` for the control units; for the ATC, weights are
+#' `(1-sp)/sp` for the treated units and 1 for the control units; for the
+#' ATE, weights are `1/sp` for the treated units and `1/(1-sp)` for the
#' control units. For cardinality matching, all matched units receive a weight
#' of 1.
#'
@@ -417,6 +409,7 @@ matchit <- function(formula,
mcall <- match.call()
## Process method
+ .chk_null_or(method, chk::chk_string)
if (length(method) == 1 && is.character(method)) {
method <- tolower(method)
method <- match_arg(method, c("exact", "cem", "nearest", "optimal", "full", "genetic", "subclass", "cardinality",
@@ -427,21 +420,19 @@ matchit <- function(formula,
fn2 <- "matchit2null"
}
else {
- stop("'method' must be the name of a supported matching method. See ?matchit for allowable options.", call. = FALSE)
+ .err("`method` must be the name of a supported matching method. See `?matchit` for allowable options")
}
#Process formula and data inputs
- if (!inherits(formula, "formula")) stop("'formula' must be a formula object.", call. = FALSE)
+ .chk_formula(formula, sides = 2)
- tt <- terms(formula, data = data)
- if (attr(tt, "response") != 1) stop("A treatment variable must be included on the left-hand side of 'formula'.", call. = FALSE)
- treat.form <- update(tt, . ~ 0)
+ treat.form <- update(terms(formula, data = data), . ~ 0)
treat.mf <- model.frame(treat.form, data = data, na.action = "na.pass")
treat <- model.response(treat.mf)
#Check and binarize treat
treat <- check_treat(treat)
- if (length(treat) == 0) stop("The treatment cannot be NULL.", call. = FALSE)
+ if (length(treat) == 0) .err("the treatment cannot be `NULL`")
names(treat) <- rownames(treat.mf)
@@ -467,28 +458,28 @@ matchit <- function(formula,
if (!is.null(s.weights)) {
if (is.character(s.weights)) {
if (is.null(data) || !is.data.frame(data)) {
- stop("If 's.weights' is specified a string, a data frame containing the named variable must be supplied to 'data'.", call. = FALSE)
+ .err("if `s.weights` is specified a string, a data frame containing the named variable must be supplied to `data`")
}
if (!all(s.weights %in% names(data))) {
- stop("The name supplied to 's.weights' must be a variable in 'data'.", call. = FALSE)
+ .err("the name supplied to `s.weights` must be a variable in `data`")
}
s.weights.form <- reformulate(s.weights)
s.weights <- model.frame(s.weights.form, data, na.action = "na.pass")
- if (ncol(s.weights) != 1) stop("'s.weights' can only contain one named variable.", call. = FALSE)
+ if (ncol(s.weights) != 1) .err("`s.weights` can only contain one named variable")
s.weights <- s.weights[[1]]
}
else if (inherits(s.weights, "formula")) {
- s.weights.form <- update(s.weights, NULL ~ .)
+ s.weights.form <- update(terms(s.weights, data = data), NULL ~ .)
s.weights <- model.frame(s.weights.form, data, na.action = "na.pass")
- if (ncol(s.weights) != 1) stop("'s.weights' can only contain one named variable.", call. = FALSE)
+ if (ncol(s.weights) != 1) .err("`s.weights` can only contain one named variable")
s.weights <- s.weights[[1]]
}
else if (!is.numeric(s.weights)) {
- stop("'s.weights' must be supplied as a numeric vector, string, or one-sided formula.", call. = FALSE)
+ .err("`s.weights` must be supplied as a numeric vector, string, or one-sided formula")
}
- if (anyNA(s.weights)) stop("Missing values are not allowed in 's.weights'.", call. = FALSE)
- if (length(s.weights) != n.obs) stop("'s.weights' must be the same length as the treatment vector.", call. = FALSE)
+ chk::chk_not_any_na(s.weights)
+ if (length(s.weights) != n.obs) .err("`s.weights` must be the same length as the treatment vector")
names(s.weights) <- names(treat)
@@ -503,19 +494,21 @@ matchit <- function(formula,
if (is.numeric(distance)) {
fn1 <- "distance2user"
}
- else if (distance %in% matchit_distances()) {
- fn1 <- "distance2mahalanobis"
- is.full.mahalanobis <- TRUE
- attr(is.full.mahalanobis, "transform") <- distance
- }
- else {
- fn1 <- paste0("distance2", distance)
+ else if (is.character(distance)) {
+ if (distance %in% matchit_distances()) {
+ fn1 <- "distance2mahalanobis"
+ is.full.mahalanobis <- TRUE
+ attr(is.full.mahalanobis, "transform") <- distance
+ }
+ else {
+ fn1 <- paste0("distance2", distance)
+ }
}
}
#Process covs
if (!is.null(fn1) && fn1 == "distance2gam") {
- check.package("mgcv")
+ rlang::check_installed("mgcv")
env <- environment(formula)
covs.formula <- mgcv::interpret.gam(formula)$fake.formula
environment(covs.formula) <- env
@@ -524,6 +517,8 @@ matchit <- function(formula,
else {
covs.formula <- delete.response(terms(formula, data = data))
}
+
+ covs.formula <- update(covs.formula, ~ .)
covs <- model.frame(covs.formula, data = data, na.action = "na.pass")
k <- ncol(covs)
for (i in seq_len(k)) {
@@ -531,8 +526,8 @@ matchit <- function(formula,
covariates.with.missingness <- names(covs)[i:k][vapply(i:k, function(j) anyNA(covs[[j]]) ||
(is.numeric(covs[[j]]) && any(!is.finite(covs[[j]]))),
logical(1L))]
- stop(paste0("Missing and non-finite values are not allowed in the covariates. Covariates with missingness or non-finite values:\n\t",
- paste(covariates.with.missingness, collapse = ", ")), call. = FALSE)
+ .err(paste0("Missing and non-finite values are not allowed in the covariates. Covariates with missingness or non-finite values:\n\t",
+ paste(covariates.with.missingness, collapse = ", ")), tidy = FALSE)
}
if (is.character(covs[[i]])) covs[[i]] <- factor(covs[[i]])
}
@@ -620,7 +615,7 @@ matchit <- function(formula,
if (!is.null(attr(caliper, "cal.formula"))) {
calcovs <- model.frame(attr(caliper, "cal.formula"), data, na.action = "na.pass")
- if (anyNA(calcovs)) stop("Missing values are not allowed in the covariates named in 'caliper'.", call. = FALSE)
+ if (anyNA(calcovs)) .err("missing values are not allowed in the covariates named in `caliper`")
attr(caliper, "cal.formula") <- NULL
}
}
@@ -645,7 +640,7 @@ matchit <- function(formula,
X.list <- list(covs, exactcovs, mahcovs, calcovs, antiexactcovs)
all.covs <- lapply(X.list, names)
for (i in seq_along(X.list)[-1]) if (!is.null(X.list[[i]])) X.list[[i]][names(X.list[[i]]) %in% unlist(all.covs[1:(i-1)])] <- NULL
- X.list[lengths(X.list) == 0] <- NULL
+ X.list[vapply(X.list, is.null, logical(1L))] <- NULL
## putting all the results together
out <- list(
@@ -669,10 +664,10 @@ matchit <- function(formula,
obj = if (include.obj) match.out[["obj"]]
)
- out[lengths(out) == 0] <- NULL
+ out[vapply(out, is.null, logical(1L))] <- NULL
class(out) <- class(match.out)
- return(out)
+ out
}
#' @export
@@ -745,5 +740,5 @@ matchit2null <- function(discarded, ...) {
res <- list(weights = as.numeric(!discarded))
class(res) <- "matchit"
- return(res)
+ res
}
diff --git a/R/matchit2cardinality.R b/R/matchit2cardinality.R
index e22ad24..b986617 100644
--- a/R/matchit2cardinality.R
+++ b/R/matchit2cardinality.R
@@ -283,7 +283,7 @@ matchit2cardinality <- function(treat, data, discarded, formula,
estimand <- toupper(estimand)
estimand <- match_arg(estimand, c("ATT", "ATC", "ATE"))
if (!is.null(focal)) {
- if (!focal %in% tvals) stop("'focal' must be a value of the treatment.", call. = FALSE)
+ if (!focal %in% tvals) .err("`focal` must be a value of the treatment")
}
else if (estimand == "ATC") {
focal <- min(tvals)
@@ -301,20 +301,20 @@ matchit2cardinality <- function(treat, data, discarded, formula,
if (!is.null(exact)) {
ex <- factor(exactify(model.frame(exact, data = data), nam = lab, sep = ", ", include_vars = TRUE))
- cc <- do.call("intersect", lapply(tvals, function(t) ex[treat == t]))
- if (length(cc) == 0) stop("No matches were found.", call. = FALSE)
+ cc <- do.call("intersect", lapply(tvals, function(t) as.integer(ex)[treat == t]))
+ if (length(cc) == 0) .err("no matches were found")
}
else {
ex <- gl(1, length(treat))
+ cc <- 1
}
#Process mahvars
if (!is.null(mahvars)) {
- if (!is.finite(ratio) || !is_whole_number(ratio)) {
- stop("`mahvars` can only be used with `method = \"cardinality\"` when `ratio` is a whole number.",
- call. = FALSE)
+ if (!is.finite(ratio) || !chk::vld_whole_number(ratio)) {
+ .err("`mahvars` can only be used with `method = \"cardinality\"` when `ratio` is a whole number")
}
- check.package("optmatch")
+ rlang::check_installed("optmatch")
mahcovs <- transform_covariates(mahvars, data = data, method = "mahalanobis",
s.weights = s.weights, treat = treat,
discarded = discarded)
@@ -332,33 +332,35 @@ matchit2cardinality <- function(treat, data, discarded, formula,
#Process tols
assign <- get_assign(X)
- if (length(tols) == 0 || !is.numeric(tols) || anyNA(tols)) stop("'tols' must be numeric.", call. = FALSE)
+ chk::chk_numeric(tols)
if (length(tols) == 1) tols <- rep(tols, ncol(X))
else if (length(tols) == max(assign)) {
tols <- tols[assign]
}
else if (length(tols) != ncol(X)) {
- stop("'tols' must have length 1 or the number of covariates. See ?method_cardinality for details.", call. = FALSE)
+ .err("`tols` must have length 1 or the number of covariates. See `?method_cardinality` for details")
}
- if (length(std.tols) == 0 || !is.logical(std.tols) || anyNA(std.tols)) stop("'std.tols' must be logical (TRUE/FALSE).", call. = FALSE)
+ chk::chk_logical(std.tols)
if (length(std.tols) == 1) std.tols <- rep(std.tols, ncol(X))
else if (length(std.tols) == max(assign)) {
std.tols <- std.tols[assign]
}
else if (length(std.tols) != ncol(X)) {
- stop("'std.tols' must have length 1 or the number of covariates. See ?method_cardinality for details.", call. = FALSE)
+ .err("`std.tols` must have length 1 or the number of covariates. See `?method_cardinality` for details")
}
#Apply std.tols
if (any(std.tols)) {
- if (estimand == "ATE") {
- sds <- sqrt(Reduce("+", lapply(tvals, function(t) {
- apply(X[treat==t, std.tols, drop = FALSE], 2, wvar, w = s.weights[treat==t])
- }))/nt)
- }
- else {
- sds <- sqrt(apply(X[treat==focal,std.tols,drop=FALSE], 2, wvar, w = s.weights[treat==focal]))
+ sds <- {
+ if (estimand == "ATE") {
+ pooled_sd(X[, std.tols, drop = FALSE], t = treat,
+ w = s.weights, contribution = "equal")
+ }
+ else {
+ sqrt(apply(X[treat == focal, std.tols, drop = FALSE], 2,
+ wvar, w = s.weights[treat == focal]))
+ }
}
zero.sds <- sds < 1e-10
@@ -369,7 +371,12 @@ matchit2cardinality <- function(treat, data, discarded, formula,
opt.out <- setNames(vector("list", nlevels(ex)), levels(ex))
- for (e in levels(ex)) {
+ for (e in levels(ex)[cc]) {
+ if (nlevels(ex) > 1 && verbose) {
+ cat(sprintf("Matching subgroup %s/%s: %s...\n",
+ match(e, levels(ex)[cc]), length(cc), e))
+ }
+
in.exact <- which(!discarded & ex == e)
treat_in.exact <- treat[in.exact]
@@ -420,8 +427,7 @@ matchit2cardinality <- function(treat, data, discarded, formula,
class(res) <- "matchit"
- return(res)
-
+ res
}
## Function to actually do the matching
@@ -439,10 +445,12 @@ cardinality_matchit <- function(treat, X, estimand = "ATT", tols = .05, s.weight
if (is.null(focal)) focal <- tvals[length(tvals)]
- if (length(time) != 1 || !is.numeric(time) || time <= 0) stop("'time' must be a positive number.", call. = FALSE)
+ chk::chk_number(time)
+ chk::chk_gt(time, 0)
+ chk::chk_string(solver)
solver <- match_arg(solver, c("glpk", "symphony", "gurobi"))
- check.package(switch(solver, glpk = "Rglpk", symphony = "Rsymphony", gurobi = "gurobi"))
+ rlang::check_installed(switch(solver, glpk = "Rglpk", symphony = "Rsymphony", gurobi = "gurobi"))
#Select match type
if (estimand == "ATE") match_type <- "profile_ate"
@@ -603,9 +611,11 @@ cardinality_matchit <- function(treat, X, estimand = "ATT", tols = .05, s.weight
cardinality_error_report(opt.out, solver)
- sol <- switch(solver, "glpk" = opt.out$solution,
+ sol <- switch(solver,
+ "glpk" = opt.out$solution,
"symphony" = opt.out$solution,
"gurobi" = opt.out$x)
+
if (match_type %in% c("profile_ate", "cardinality")) {
weights <- round(sol[seq_len(n)])
}
@@ -627,34 +637,32 @@ cardinality_matchit <- function(treat, X, estimand = "ATT", tols = .05, s.weight
}
}
- return(list(weights = weights, opt.out = opt.out))
+ list(weights = weights, opt.out = opt.out)
}
cardinality_error_report <- function(out, solver) {
if (solver == "glpk") {
if (out$status == 1) {
if (all(out$solution == 0)) {
- stop("The optimization problem may be infeasible. Try increasing the value of 'tols'.\nSee ?method_cardinality for additional details.", call. = FALSE)
- }
- else {
- warning("The optimizer failed to find an optimal solution in the time alotted. The returned solution may not be optimal.\nSee ?method_cardinality for additional details.", call. = FALSE)
+ .err("the optimization problem may be infeasible. Try increasing the value of `tols`.\nSee `?method_cardinality` for additional details")
}
+ .wrn("the optimizer failed to find an optimal solution in the time alotted. The returned solution may not be optimal.\nSee `?method_cardinality` for additional details")
}
}
else if (solver == "symphony") {
if (names(out$status) %in% c("TM_TIME_LIMIT_EXCEEDED") && !all(out$solution == 0) && all(out$solution <= 1)) {
- warning("The optimizer failed to find an optimal solution in the time alotted. The returned solution may not be optimal.", call. = FALSE)
+ .wrn("the optimizer failed to find an optimal solution in the time alotted. The returned solution may not be optimal")
}
else if (names(out$status) != "TM_OPTIMAL_SOLUTION_FOUND") {
- stop("The optimizer failed to find an optimal solution in the time alotted. The optimization problem may be infeasible. Try increasing the value of 'tols'.\nSee ?method_cardinality for additional details.", call. = FALSE)
+ .err("the optimizer failed to find an optimal solution in the time alotted. The optimization problem may be infeasible. Try increasing the value of 'tols'.\nSee `?method_cardinality` for additional details")
}
}
else if (solver == "gurobi") {
if (out$status %in% c("TIME_LIMIT", "SUBOPTIMAL") && !all(out$x == 0)) {
- warning("The optimizer failed to find an optimal solution in the time alotted. The returned solution may not be optimal.\nSee ?method_cardinality for additional details.", call. = FALSE)
+ .wrn("the optimizer failed to find an optimal solution in the time alotted. The returned solution may not be optimal.\nSee `?method_cardinality` for additional details")
}
else if (out$status %in% c("INFEASIBLE", "INF_OR_UNBD", "NUMERIC") || all(out$x == 0)) {
- stop("The optimization problem may be infeasible. Try increasing the value of 'tols'.\nSee ?method_cardinality for additional details.", call. = FALSE)
+ .err("The optimization problem may be infeasible. Try increasing the value of `tols`.\nSee `?method_cardinality` for additional details")
}
}
}
diff --git a/R/matchit2cem.R b/R/matchit2cem.R
index 16552ae..1f396b6 100644
--- a/R/matchit2cem.R
+++ b/R/matchit2cem.R
@@ -128,6 +128,8 @@
#' solution that balances covariate balance and remaining sample size is
#' obtained. The arguments are described below.
#'
+#' ### `grouping`
+#'
#' The argument to `grouping` must be a list, where each component has the
#' name of a categorical variable, the levels of which are to be combined. Each
#' component must itself be a list; this list contains one or more vectors of
@@ -141,7 +143,9 @@
#' omitted from the previous code). Note that if a categorical variable does
#' not appear in `grouping`, it will not be coarsened, so exact matching
#' will take place on it. `grouping` should not be used for numeric
-#' variables; use `cutpoints`, described below, instead.
+#' variables with more than a few values; use `cutpoints`, described below, instead.
+#'
+#' ### `cutpoints`
#'
#' The argument to `cutpoints` must also be a list, where each component
#' has the name of a numeric variables that is to be binned. (As a shortcut, it
@@ -150,7 +154,7 @@
#' separate the bins, a single number giving the number of bins, or a string
#' corresponding to an algorithm used to compute the number of bins. Any values
#' at a boundary will be placed into the higher bin; e.g., if the cutpoints
-#' were `(c(0, 5, 10))`, values of 5 would be placed into the same bin as
+#' were `c(0, 5, 10)`, values of 5 would be placed into the same bin as
#' values of 6, 7, 8, or 9, and values of 10 would be placed into a different
#' bin. Internally, values of `-Inf` and `Inf` are appended to the
#' beginning and end of the range. When given as a single number defining the
@@ -259,7 +263,9 @@
NULL
matchit2cem <- function(treat, covs, estimand = "ATT", s.weights = NULL, verbose = FALSE, ...) {
- if (length(covs) == 0) stop("Covariates must be specified in the input formula to use coarsened exact matching.", call. = FALSE)
+ if (length(covs) == 0) {
+ .err("Covariates must be specified in the input formula to use coarsened exact matching")
+ }
if (verbose) cat("Coarsened exact matching...\n")
@@ -287,15 +293,18 @@ matchit2cem <- function(treat, covs, estimand = "ATT", s.weights = NULL, verbose
res <- list(match.matrix = mm,
subclass = strat,
- weights = weights.subclass(strat, treat, estimand))
+ weights = get_weights_from_subclass(strat, treat, estimand))
if (verbose) cat("Done.\n")
class(res) <- "matchit"
- return(res)
+
+ res
}
-cem_matchit <- function(treat, X, cutpoints = "sturges", grouping = list(), k2k = FALSE, k2k.method = "mahalanobis", mpower = 2, s.weights = NULL, estimand = "ATT") {
+cem_matchit <- function(treat, X, cutpoints = "sturges", grouping = list(), k2k = FALSE,
+ k2k.method = "mahalanobis", mpower = 2, s.weights = NULL,
+ estimand = "ATT") {
#In-house implementation of cem. Basically the same except:
#treat is a vector if treatment status, not the name of a variable
#X is a data.frame of covariates
@@ -306,14 +315,9 @@ cem_matchit <- function(treat, X, cutpoints = "sturges", grouping = list(), k2k
if (k2k) {
if (length(unique(treat)) > 2) {
- stop("`k2k` cannot be `TRUE` with a multi-cateory treatment.", call. = FALSE)
+ .err("`k2k` cannot be `TRUE` with a multi-category treatment")
}
if (!is.null(k2k.method)) {
- # X.match <- scale(get.covs.matrix(data = X), center = FALSE)
- # k2k.method <- tolower(k2k.method)
- # k2k.method <- match_arg(k2k.method, c("mahalanobis", "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"))
- # if (k2k.method == "mahalanobis") mahSigma_inv <- generalized_inverse(cov(X.match))
-
k2k.method <- tolower(k2k.method)
k2k.method <- match_arg(k2k.method, c(matchit_distances(), "maximum", "manhattan", "canberra", "binary", "minkowski"))
@@ -321,66 +325,132 @@ cem_matchit <- function(treat, X, cutpoints = "sturges", grouping = list(), k2k
method = if (k2k.method %in% matchit_distances()) k2k.method else "euclidean")
}
}
+
+ for (i in names(X)) {
+ if (is.ordered(X[[i]])) X[[i]] <- as.numeric(X[[i]])
+ }
is.numeric.cov <- setNames(vapply(X, is.numeric, logical(1L)), names(X))
+ #Process grouping
+ if (length(grouping) > 0) {
+ if (!is.list(grouping) || is.null(names(grouping))) {
+ .err("`grouping` must be a named list of grouping values with an element for each variable whose values are to be binned")
+ }
+
+ bad.names <- setdiff(names(grouping), names(X))
+ nb <- length(bad.names)
+ if (nb > 0) {
+ .wrn(sprintf("the variable%%s %s named in `grouping` %%r not in the variables supplied to `matchit()` and will be ignored",
+ word_list(bad.names, quotes = 2, and.or = "and")), n = nb)
+ grouping[bad.names] <- NULL
+ }
+
+ for (i in names(grouping)) {
+ X[[i]] <- as.character(X[[i]])
+ }
+
+ bag.groupings <- names(grouping)[vapply(grouping, function(g) {
+ !is.list(g) ||
+ !all(vapply(g, function(gg) is.atomic(gg) && is.vector(gg), logical(1L)))
+ }, logical(1L))]
+ nbg <- length(bag.groupings)
+ if (nbg > 0) {
+ .err(paste0("Each entry in the list supplied to `groupings` must be a list with entries containing values of the corresponding variable.",
+ "\nIncorrectly specified variable%s:\n\t"),
+ paste(bag.groupings, collapse = ", "),
+ tidy = FALSE, n = nbg)
+ }
+
+ for (g in names(grouping)) {
+ x <- X[[g]]
+ groups <- grouping[[g]]
+
+ for (i in seq_along(groups)) {
+ groups[[i]] <- as.character(groups[[i]])
+ x[x %in% groups[[i]]] <- groups[[i]][1]
+ }
+ X[[g]] <- x
+
+ #Remove cutpoints if variable named in `grouping`
+ is.numeric.cov[g] <- FALSE
+ }
+ }
+
#Process cutpoints
if (!is.list(cutpoints)) {
- cutpoints <- setNames(as.list(rep(cutpoints, sum(is.numeric.cov))), names(X)[is.numeric.cov])
+ cutpoints <- setNames(lapply(names(X)[is.numeric.cov], function(i) cutpoints), names(X)[is.numeric.cov])
}
- if (is.null(names(cutpoints))) stop("`cutpoints` must be a named list of binning values with an element for each numeric variable.", call. = FALSE)
+ if (is.null(names(cutpoints))) {
+ .err("`cutpoints` must be a named list of binning values with an element for each numeric variable")
+ }
bad.names <- setdiff(names(cutpoints), names(X))
nb <- length(bad.names)
if (nb > 0) {
- warning(paste0(ngettext(nb, "The variable ", "The variables "),
- word_list(bad.names, quotes = 2), " named in `cutpoints` ",
- ngettext(nb, "is ", "are "), "not in the variables supplied to matchit() and will be ignored."),
- immediate. = TRUE, call. = FALSE)
+ .wrn(sprintf("the variable%%s %s named in `cutpoints` %%r not in the variables supplied to `matchit()` and will be ignored",
+ word_list(bad.names, quotes = 2, and.or = "and")), n = nb)
cutpoints[bad.names] <- NULL
}
+ if (length(grouping) > 0) {
+ grouping.cutpoint.names <- intersect(names(grouping), names(cutpoints))
+ ngc <- length(grouping.cutpoint.names)
+ if (ngc > 0) {
+ .wrn(sprintf("the variable%%s %s %%r named in both `grouping` and `cutpoints`; %s entr%%y%%s in `cutpoints` will be ignored",
+ word_list(grouping.cutpoint.names, quotes = 2, and.or = "and"),
+ ngettext(ngc, "its", "their")), n = ngc)
+ cutpoints[grouping.cutpoint.names] <- NULL
+ }
+ }
+
+ non.numeric.in.cutpoints <- intersect(names(X)[!is.numeric.cov], names(cutpoints))
+ nnnic <- length(non.numeric.in.cutpoints)
+ if (nnnic > 0) {
+ .wrn(sprintf("the variable%%s %s named in `cutpoints` %%r not numeric and %s cutpoints will not be applied. Use `grouping` for non-numeric variables",
+ word_list(non.numeric.in.cutpoints, quotes = 2, and.or = "and"),
+ ngettext(nnnic, "its", "their")), n = nnnic)
+ }
+
bad.cuts <- setNames(rep(FALSE, length(cutpoints)), names(cutpoints))
for (i in names(cutpoints)) {
if (length(cutpoints[[i]]) == 0) {
cutpoints[[i]] <- "sturges"
}
else if (length(cutpoints[[i]]) == 1) {
- if (is.character(cutpoints[[i]])) {
-
+ if (is.na(cutpoints[[i]])) is.numeric.cov[i] <- FALSE #Will not be binned
+ else if (is.character(cutpoints[[i]])) {
bad.cuts[i] <- !(startsWith(cutpoints[[i]], "q") && can_str2num(substring(cutpoints[[i]], 2))) &&
is.na(pmatch(cutpoints[[i]], c("sturges", "fd", "scott")))
}
else if (is.numeric(cutpoints[[i]])) {
- if (!is.finite(cutpoints[[i]]) || cutpoints[[i]] < 0) bad.cuts[i] <- TRUE
- if (cutpoints[[i]] == 0) is.numeric.cov[i] <- FALSE #Will not be binned
- else if (cutpoints[[i]] == 1) X[[i]] <- NULL #Removing from X, still in X.match
+ if (!is.finite(cutpoints[[i]]) || cutpoints[[i]] < 0) {
+ bad.cuts[i] <- TRUE
+ }
+ else if (cutpoints[[i]] == 0) {
+ is.numeric.cov[i] <- FALSE #Will not be binned
+ }
+ else if (cutpoints[[i]] == 1) {
+ X[[i]] <- NULL #Removing from X, still in X.match
+ is.numeric.cov <- is.numeric.cov[names(is.numeric.cov) != i]
+ }
+ }
+ else {
+ bad.cuts[i] <- TRUE
}
}
else {
bad.cuts[i] <- !is.numeric(cutpoints[[i]])
}
}
+
if (any(bad.cuts)) {
- stop(paste0("All entries in the list supplied to `cutpoints` must be one of the following:",
+ .err(paste0("All entries in the list supplied to `cutpoints` must be one of the following:",
"\n\t- a string containing the name of an allowable binning method",
"\n\t- a single number corresponding to the number of bins",
"\n\t- a numeric vector containing the cut points separating bins",
- "\nIncorrectly specified ", ngettext(sum(bad.cuts), "variable:\n\t", "variables:\n\t"),
- paste(names(cutpoints)[bad.cuts], collapse = ", ")), call. = FALSE)
- }
-
- #Process grouping
- if (!is.null(grouping) && !is.null(names(grouping))) {
- X[names(grouping)] <- lapply(names(grouping), function(g) {
- x <- X[[g]]
- groups <- grouping[[g]]
-
- for (i in seq_along(groups)) {
- x[x %in% groups[[i]]] <- groups[[i]][1]
- }
- x
- })
- cutpoints[names(cutpoints) %in% names(grouping)] <- NULL
+ "\nIncorrectly specified variable%s:\n\t"),
+ paste(names(cutpoints)[bad.cuts], collapse = ", "),
+ tidy = FALSE, n = sum(bad.cuts))
}
#Create bins for numeric variables
@@ -416,15 +486,20 @@ cem_matchit <- function(treat, X, cutpoints = "sturges", grouping = list(), k2k
X[[i]] <- findInterval(X[[i]], breaks)
}
- #Exact match
- xx <- exactify(X, names(treat))
- cc <- do.call("intersect", unname(split(xx, treat)))
-
- if (length(cc) == 0) {
- stop("No units were matched. Try coarsening the variables further or decrease the number of variables to match on.", call. = FALSE)
+ if (length(X) == 0) {
+ subclass <- setNames(rep(1, length(treat)), names(treat))
}
+ else {
+ #Exact match
+ xx <- exactify(X, names(treat))
+ cc <- do.call("intersect", unname(split(xx, treat)))
+
+ if (length(cc) == 0) {
+ .err("no units were matched. Try coarsening the variables further or decrease the number of variables to match on")
+ }
- subclass <- setNames(match(xx, cc), names(treat))
+ subclass <- setNames(match(xx, cc), names(treat))
+ }
extra.sub <- max(subclass, na.rm = TRUE)
@@ -469,14 +544,12 @@ cem_matchit <- function(treat, X, cutpoints = "sturges", grouping = list(), k2k
}
#If any unmatched units remain, give them NA subclass
- if (any(dim(dist.mat) > 0)) subclass[unlist(dimnames(dist.mat))] <- NA_integer_
+ if (any(dim(dist.mat) > 0)) is.na(subclass)[unlist(dimnames(dist.mat))] <- TRUE
}
}
subclass <- factor(subclass, nmax = extra.sub)
- names(subclass) <- names(treat)
-
- return(subclass)
-}
\ No newline at end of file
+ setNames(subclass, names(treat))
+}
diff --git a/R/matchit2exact.R b/R/matchit2exact.R
index 3231f30..ecf3bba 100644
--- a/R/matchit2exact.R
+++ b/R/matchit2exact.R
@@ -92,7 +92,7 @@ matchit2exact <- function(treat, covs, data, estimand = "ATT", verbose = FALSE,
if(verbose)
cat("Exact matching... \n")
- if (length(covs) == 0) stop("Covariates must be specified in the input formula to use exact matching.", call. = FALSE)
+ if (length(covs) == 0) .err("covariates must be specified in the input formula to use exact matching")
estimand <- toupper(estimand)
estimand <- match_arg(estimand, c("ATT", "ATC", "ATE"))
@@ -101,7 +101,7 @@ matchit2exact <- function(treat, covs, data, estimand = "ATT", verbose = FALSE,
cc <- do.call("intersect", lapply(unique(treat), function(t) xx[treat == t]))
if (length(cc) == 0) {
- stop("No exact matches were found.", call. = FALSE)
+ .err("No exact matches were found")
}
psclass <- setNames(factor(match(xx, cc), nmax = length(cc)), names(treat))
@@ -109,10 +109,10 @@ matchit2exact <- function(treat, covs, data, estimand = "ATT", verbose = FALSE,
if (verbose) cat("Calculating matching weights... ")
res <- list(subclass = psclass,
- weights = weights.subclass(psclass, treat, estimand))
+ weights = get_weights_from_subclass(psclass, treat, estimand))
if (verbose) cat("Done.\n")
class(res) <- "matchit"
- return(res)
+ res
}
diff --git a/R/matchit2full.R b/R/matchit2full.R
index dfaa0f7..4bc10f0 100644
--- a/R/matchit2full.R
+++ b/R/matchit2full.R
@@ -228,7 +228,7 @@ matchit2full <- function(treat, formula, data, distance, discarded,
estimand = "ATT", verbose = FALSE,
is.full.mahalanobis, antiexact = NULL, ...) {
- check.package("optmatch")
+ rlang::check_installed("optmatch")
if (verbose) cat("Full matching... \n")
@@ -259,8 +259,8 @@ matchit2full <- function(treat, formula, data, distance, discarded,
if (is.full.mahalanobis) {
if (length(attr(terms(formula, data = data), "term.labels")) == 0) {
- stop(sprintf("Covariates must be specified in the input formula when distance = \"%s\".",
- attr(is.full.mahalanobis, "transform")), call. = FALSE)
+ .err(sprintf("covariates must be specified in the input formula when `distance = \"%s\"`",
+ attr(is.full.mahalanobis, "transform")))
}
mahvars <- formula
}
@@ -272,11 +272,12 @@ matchit2full <- function(treat, formula, data, distance, discarded,
if (!is.null(exact)) {
ex <- factor(exactify(model.frame(exact, data = data),
sep = ", ", include_vars = TRUE)[!discarded])
- cc <- intersect(ex[treat_==1], ex[treat_==0])
- if (length(cc) == 0) stop("No matches were found.", call. = FALSE)
+ cc <- intersect(as.integer(ex)[treat_==1], as.integer(ex)[treat_==0])
+ if (length(cc) == 0) .err("No matches were found")
}
else {
ex <- factor(rep("_", length(treat_)), levels = "_")
+ cc <- 1
}
#Create distance matrix; note that Mahalanobis distance computed using entire
@@ -316,7 +317,7 @@ matchit2full <- function(treat, formula, data, distance, discarded,
#Process caliper
if (!is.null(caliper)) {
if (min.controls != 0) {
- stop("Calipers cannot be used with method = \"full\" when 'min.controls' is specified.", call. = FALSE)
+ .err("calipers cannot be used with `method = \"full\"` when `min.controls` is specified")
}
if (any(names(caliper) != "")) {
@@ -345,38 +346,35 @@ matchit2full <- function(treat, formula, data, distance, discarded,
t_df <- data.frame(treat)
- for (e in levels(ex)) {
+ for (e in levels(ex)[cc]) {
if (nlevels(ex) > 1) {
+ if (verbose) {
+ cat(sprintf("Matching subgroup %s/%s: %s...\n",
+ match(e, levels(ex)[cc]), length(cc), e))
+ }
mo_ <- mo[ex[treat_==1] == e, ex[treat_==0] == e]
}
else mo_ <- mo
if (any(dim(mo_) == 0) || !any(is.finite(mo_))) next
- else if (all(dim(mo_) == 1)) {
+ else if (all(dim(mo_) == 1) && all(is.finite(mo_))) {
pair[ex == e] <- paste(1, e, sep = "|")
next
}
- withCallingHandlers({
+ matchit_try({
p[[e]] <- do.call(optmatch::fullmatch,
c(list(mo_,
min.controls = min.controls,
max.controls = max.controls,
data = t_df), #just to get rownames; not actually used in matching
A))
- },
- warning = function(w) {
- warning(paste0("(from optmatch) ", conditionMessage(w)), call. = FALSE, immediate. = TRUE)
- invokeRestart("muffleWarning")
- },
- error = function(e1) {
- stop(paste0("(from optmatch) ", conditionMessage(e1)), call. = FALSE)
- })
+ }, from = "optmatch")
pair[names(p[[e]])[!is.na(p[[e]])]] <- paste(as.character(p[[e]][!is.na(p[[e]])]), e, sep = "|")
}
- if (all(is.na(pair))) stop("No matches were found.", call. = FALSE)
+ if (all(is.na(pair))) .err("No matches were found")
if (length(p) == 1) p <- p[[1]]
psclass <- factor(pair)
@@ -389,11 +387,11 @@ matchit2full <- function(treat, formula, data, distance, discarded,
if (verbose) cat("Calculating matching weights... ")
res <- list(subclass = psclass,
- weights = weights.subclass(psclass, treat, estimand),
+ weights = get_weights_from_subclass(psclass, treat, estimand),
obj = p)
if (verbose) cat("Done.\n")
class(res) <- c("matchit")
- return(res)
+ res
}
diff --git a/R/matchit2genetic.R b/R/matchit2genetic.R
index 430941b..9c5c392 100644
--- a/R/matchit2genetic.R
+++ b/R/matchit2genetic.R
@@ -93,9 +93,16 @@
#' to `GenMatch()` for use in computing the balance t-test p-values in the
#' process of matching.
#' @param replace whether matching should be done with replacement.
-#' @param m.order the order that the matching takes place. The default is
-#' `"largest"` when `distance` corresponds to a propensity score and
-#' `"data"` otherwise. See [matchit()] for allowable options.
+#' @param m.order the order that the matching takes place. Allowable options
+#' include `"largest"`, where matching takes place in descending order of
+#' distance measures; `"smallest"`, where matching takes place in ascending
+#' order of distance measures; `"random"`, where matching takes place
+#' in a random order; and `"data"` where matching takes place based on the
+#' order of units in the data. When `m.order = "random"`, results may differ
+#' across different runs of the same code unless a seed is set and specified
+#' with [set.seed()]. The default of `NULL` corresponds to `"largest"` when a
+#' propensity score is estimated or supplied as a vector and `"data"`
+#' otherwise.
#' @param caliper the width(s) of the caliper(s) used for caliper matching. See
#' Details and Examples.
#' @param std.caliper `logical`; when calipers are specified, whether they
@@ -246,7 +253,7 @@ matchit2genetic <- function(treat, data, distance, discarded,
is.full.mahalanobis, use.genetic = TRUE,
antiexact = NULL, ...) {
- check.package(c("Matching", "rgenoud"))
+ rlang::check_installed(c("Matching", "rgenoud"))
if (verbose) cat("Genetic matching... \n")
@@ -265,12 +272,12 @@ matchit2genetic <- function(treat, data, distance, discarded,
if (!replace) {
if (sum(!discarded & treat != focal) < sum(!discarded & treat == focal)) {
- warning(sprintf("Fewer %s units than %s units; not all %s units will get a match.",
- tc[2], tc[1], tc[1]), immediate. = TRUE, call. = FALSE)
+ .wrn(sprintf("fewer %s units than %s units; not all %s units will get a match",
+ tc[2], tc[1], tc[1]))
}
else if (sum(!discarded & treat != focal) < sum(!discarded & treat == focal)*ratio) {
- stop(sprintf("Not enough %s units for %s matches for each %s unit.",
- tc[2], ratio, tc[1]), call. = FALSE)
+ .err(sprintf("not enough %s units for %s matches for each %s unit",
+ tc[2], ratio, tc[1]))
}
}
@@ -308,7 +315,7 @@ matchit2genetic <- function(treat, data, distance, discarded,
}
if (ncol(covs_to_balance) == 0) {
- stop("Covariates must be specified in the input formula to use genetic matching.", call. = FALSE)
+ .err("covariates must be specified in the input formula to use genetic matching")
}
#Process exact; exact.log will be supplied to GenMatch() and Match()
@@ -317,7 +324,7 @@ matchit2genetic <- function(treat, data, distance, discarded,
ex <- as.integer(factor(exactify(model.frame(exact, data = data), names(treat), sep = ", ", include_vars = TRUE)))
cc <- intersect(ex[treat==1], ex[treat==0])
- if (length(cc) == 0) stop("No matches were found.", call. = FALSE)
+ if (length(cc) == 0) .err("No matches were found")
X <- cbind(X, ex)
@@ -404,7 +411,7 @@ matchit2genetic <- function(treat, data, distance, discarded,
}
if (use.genetic) {
- withCallingHandlers({
+ matchit_try({
g.out <- do.call(Matching::GenMatch,
c(list(Tr = treat_, X = X, BalanceMatrix = covs_to_balance,
M = ratio, exact = exact.log, caliper = cal,
@@ -412,16 +419,8 @@ matchit2genetic <- function(treat, data, distance, discarded,
CommonSupport = FALSE, verbose = verbose,
weights = s.weights, print.level = 2*verbose),
A[names(A) %in% names(formals(Matching::GenMatch))]))
- },
- warning = function(w) {
- if (!startsWith(conditionMessage(w), "replace==FALSE, but there are more (weighted) treated obs than control obs.")) {
- warning(paste0("(from Matching) ", conditionMessage(w)), call. = FALSE, immediate. = TRUE)
- }
- invokeRestart("muffleWarning")
- },
- error = function(e) {
- stop(paste0("(from Matching) ", conditionMessage(e)), call. = FALSE)
- })
+ }, from = "Matching",
+ dont_warn_if = "replace==FALSE, but there are more (weighted) treated obs than control obs.")
}
else {
#For debugging
@@ -429,14 +428,14 @@ matchit2genetic <- function(treat, data, distance, discarded,
}
lab <- names(treat)
- lab1 <- names(treat[treat == 1])
+ lab1 <- lab[treat == 1]
lab_ <- names(treat_)
ind_ <- seq_along(treat)[ord]
# if (!isFALSE(A$use.Match)) {
- withCallingHandlers({
+ matchit_try({
m.out <- Matching::Match(Tr = treat_, X = X,
M = ratio, exact = exact.log, caliper = cal,
replace = replace, estimand = "ATT", ties = FALSE,
@@ -446,16 +445,8 @@ matchit2genetic <- function(treat, data, distance, discarded,
else if (is.null(s.weights)) generalized_inverse(cor(X))
else generalized_inverse(cov.wt(X, s.weights, cor = TRUE)$cor),
restrict = A[["restrict"]], version = "fast")
- },
- warning = function(w) {
- if (!startsWith(conditionMessage(w), "replace==FALSE, but there are more (weighted) treated obs than control obs.")) {
- warning(paste0("(from Matching) ", conditionMessage(w)), call. = FALSE, immediate. = TRUE)
- }
- invokeRestart("muffleWarning")
- },
- error = function(e) {
- stop(paste0("(from Matching) ", conditionMessage(e)), call. = FALSE)
- })
+ }, from = "Matching",
+ dont_warn_if = "replace==FALSE, but there are more (weighted) treated obs than control obs.")
#Note: must use character match.matrix because of re-ordering treat into treat_
mm <- matrix(NA_integer_, nrow = n1, ncol = max(table(m.out$index.treated)),
@@ -503,11 +494,11 @@ matchit2genetic <- function(treat, data, distance, discarded,
res <- list(match.matrix = nummm2charmm(mm, treat),
subclass = psclass,
- weights = weights.matrix(mm, treat),
+ weights = get_weights_from_mm(mm, treat),
obj = g.out)
if (verbose) cat("Done.\n")
class(res) <- "matchit"
- return(res)
+ res
}
diff --git a/R/matchit2nearest.R b/R/matchit2nearest.R
index 06a9abc..f8878cf 100644
--- a/R/matchit2nearest.R
+++ b/R/matchit2nearest.R
@@ -70,9 +70,17 @@
#' @param s.weights the variable containing sampling weights to be incorporated
#' into propensity score models and balance statistics.
#' @param replace whether matching should be done with replacement.
-#' @param m.order the order that the matching takes place. The default is
-#' `"largest"` when `distance` corresponds to a propensity score and
-#' `"data"` otherwise. See [matchit()] for allowable options.
+#' @param m.order the order that the matching takes place. Allowable options
+#' include `"largest"`, where matching takes place in descending order of
+#' distance measures; `"smallest"`, where matching takes place in ascending
+#' order of distance measures; `"closest"`, where matching takes place in
+#' order of the distance between units; `"random"`, where matching takes place
+#' in a random order; and `"data"` where matching takes place based on the
+#' order of units in the data. When `m.order = "random"`, results may differ
+#' across different runs of the same code unless a seed is set and specified
+#' with [set.seed()]. The default of `NULL` corresponds to `"largest"` when a
+#' propensity score is estimated or supplied as a vector and `"data"`
+#' otherwise.
#' @param caliper the width(s) of the caliper(s) used for caliper matching. See
#' Details and Examples.
#' @param std.caliper `logical`; when calipers are specified, whether they
@@ -211,6 +219,10 @@
#' `ratio`, and `max.controls` must be greater than `ratio`. See
#' Examples below for an example of their use.
#'
+#' ## Using `m.order = "closest"`
+#'
+#' As of version 4.6.0, `m.order` can be set to `"closest"`, which works regardless of how the distance measure is specified. This matches in order of the distance between units. The closest pair of units across all potential pairs of units will be matched first; the second closest pair of all potential pairs will be matched second, etc. This ensures that the best possible matches are given priority, and in that sense performs similarly to `m.order = "smallest"`.
+#'
#' @seealso [matchit()] for a detailed explanation of the inputs and outputs of
#' a call to `matchit()`.
#'
@@ -266,15 +278,15 @@
#'
NULL
-matchit2nearest <- function(treat, data, distance, discarded,
+matchit2nearest <- function(treat, data, distance, discarded,
ratio = 1, s.weights = NULL, replace = FALSE, m.order = NULL,
caliper = NULL, mahvars = NULL, exact = NULL,
formula = NULL, estimand = "ATT", verbose = FALSE,
- is.full.mahalanobis, fast = TRUE,
+ is.full.mahalanobis,
antiexact = NULL, unit.id = NULL, ...){
if (verbose) {
- if (fast) check.package("RcppProgress")
+ rlang::check_installed("RcppProgress")
cat("Nearest neighbor matching... \n")
}
@@ -293,8 +305,8 @@ matchit2nearest <- function(treat, data, distance, discarded,
if (is.full.mahalanobis) {
if (length(attr(terms(formula, data = data), "term.labels")) == 0) {
- stop(sprintf("Covariates must be specified in the input formula when distance = \"%s\".",
- attr(is.full.mahalanobis, "transform")), call. = FALSE)
+ .err(sprintf("covariates must be specified in the input formula when `distance = \"%s\"`",
+ attr(is.full.mahalanobis, "transform")))
}
mahvars <- formula
}
@@ -323,6 +335,8 @@ matchit2nearest <- function(treat, data, distance, discarded,
else if (is.matrix(distance)) {
distance_mat <- distance
distance <- NULL
+
+ if (focal == 0) distance_mat <- t(distance_mat)
}
#Process caliper
@@ -381,7 +395,7 @@ matchit2nearest <- function(treat, data, distance, discarded,
ex <- factor(exactify(model.frame(exact, data = data), nam = lab, sep = ", ", include_vars = TRUE))
cc <- intersect(as.integer(ex)[treat==1], as.integer(ex)[treat==0])
- if (length(cc) == 0) stop("No matches were found.", call. = FALSE)
+ if (length(cc) == 0) .err("No matches were found")
if (reuse.max < n1) {
@@ -391,16 +405,16 @@ matchit2nearest <- function(treat, data, distance, discarded,
}, numeric(1L))
if (any(e_ratios < 1)) {
- warning(sprintf("Fewer %s units than %s units in some 'exact' strata; not all %s units will get a match.",
- tc[2], tc[1], tc[1]), immediate. = TRUE, call. = FALSE)
+ .wrn(sprintf("fewer %s units than %s units in some `exact` strata; not all %s units will get a match",
+ tc[2], tc[1], tc[1]))
}
if (ratio > 1 && any(e_ratios < ratio)) {
if (is.null(max.controls) || ratio == max.controls)
- warning(sprintf("Not all %s units will get %s matches.",
- tc[1], ratio), immediate. = TRUE, call. = FALSE)
+ .wrn(sprintf("not all %s units will get %s matches",
+ tc[1], ratio))
else
- warning(sprintf("Not enough %s units for an average of %s matches per %s unit in all 'exact' strata.",
- tc[2], ratio, tc[1]), immediate. = TRUE, call. = FALSE)
+ .wrn(sprintf("not enough %s units for an average of %s matches per %s unit in all `exact` strata",
+ tc[2], ratio, tc[1]))
}
}
}
@@ -415,19 +429,16 @@ matchit2nearest <- function(treat, data, distance, discarded,
}
if (e_ratios < 1) {
- warning(sprintf("Fewer %s %s than %s units; not all %s units will get a match.",
- tc[2], if (is.null(unit.id)) "units" else "unit IDs", tc[1], tc[1]),
- immediate. = TRUE, call. = FALSE)
+ .wrn(sprintf("fewer %s %s than %s units; not all %s units will get a match",
+ tc[2], if (is.null(unit.id)) "units" else "unit IDs", tc[1], tc[1]))
}
else if (e_ratios < ratio) {
if (is.null(max.controls) || ratio == max.controls)
- warning(sprintf("Not all %s units will get %s matches.",
- tc[1], ratio),
- immediate. = TRUE, call. = FALSE)
+ .wrn(sprintf("not all %s units will get %s matches",
+ tc[1], ratio))
else
- warning(sprintf("Not enough %s %s for an average of %s matches per %s unit.",
- tc[2], if (is.null(unit.id)) "units" else "unit IDs", ratio, tc[1]),
- immediate. = TRUE, call. = FALSE)
+ .wrn(sprintf("not enough %s %s for an average of %s matches per %s unit",
+ tc[2], if (is.null(unit.id)) "units" else "unit IDs", ratio, tc[1]))
}
}
}
@@ -436,13 +447,13 @@ matchit2nearest <- function(treat, data, distance, discarded,
#Each treated unit get its own value of ratio
if (!is.null(max.controls)) {
if (is.null(distance)) {
- if (is.full.mahalanobis) stop(sprintf("'distance' cannot be \"%s\" for variable ratio matching.",
- transform), call. = FALSE)
- else stop("'distance' cannot be supplied as a matrix for variable ratio matching.", call. = FALSE)
+ if (is.full.mahalanobis) .err(sprintf("`distance` cannot be \"%s\" for variable ratio matching",
+ transform))
+ .err("`distance` cannot be supplied as a matrix for variable ratio matching")
}
m <- round(ratio * n1)
- # if (m > sum(treat == 0)) stop("'ratio' must be less than or equal to n0/n1.", call. = FALSE)
+ # if (m > sum(treat == 0)) stop("'ratio' must be less than or equal to n0/n1")
kmax <- floor((m - min.controls*(n1-1)) / (max.controls - min.controls))
kmin <- n1 - kmax - 1
@@ -469,33 +480,56 @@ matchit2nearest <- function(treat, data, distance, discarded,
}
m.order <- {
- if (is.null(distance)) match_arg(m.order, c("data", "random"))
- else if (!is.null(m.order)) match_arg(m.order, c("largest", "smallest", "random", "data"))
+ if (is.null(distance)) match_arg(m.order, c("data", "random", "closest"))
+ else if (!is.null(m.order)) match_arg(m.order, c("largest", "smallest", "data", "random", "closest"))
else if (estimand == "ATC") "smallest"
else "largest"
}
if (is.null(ex) || !is.null(unit.id)) {
- ord <- switch(m.order,
- "largest" = order(distance[treat == 1], decreasing = TRUE),
- "smallest" = order(distance[treat == 1], decreasing = FALSE),
- "random" = sample.int(n1),
- "data" = seq_len(n1))
+ if (m.order == "closest") {
+ if (!is.null(mahcovs)) {
+ distance_mat <- eucdist_internal(mahcovs, treat)
+
+ # If caliper on PS (distance), treat it as a covariate
+ if (!is.null(distance) && !is.null(caliper.dist)) {
+ caliper.covs.mat <- {
+ if (is.null(caliper.covs)) as.matrix(distance)
+ else cbind(caliper.covs.mat, distance)
+ }
+ caliper.covs <- c(caliper.covs, caliper.dist)
+ caliper.dist <- NULL
+ }
+ }
+ else if (is.null(distance_mat)) {
+ distance_mat <- eucdist_internal(distance, treat)
+ }
+
+ ord <- NULL
+ }
+ else {
+ ord <- switch(m.order,
+ "largest" = order(distance[treat == 1], decreasing = TRUE),
+ "smallest" = order(distance[treat == 1], decreasing = FALSE),
+ "random" = sample.int(n1),
+ "data" = seq_len(n1))
+ }
mm <- nn_matchC_dispatch(treat, ord, ratio, discarded, reuse.max, distance, distance_mat,
ex, caliper.dist, caliper.covs, caliper.covs.mat, mahcovs,
- antiexactcovs, unit.id, verbose)
+ antiexactcovs, unit.id, m.order, verbose)
}
else {
distance_ <- caliper.covs.mat_ <- mahcovs_ <- antiexactcovs_ <- distance_mat_ <- NULL
- mm_list <- lapply(levels(ex), function(e) {
+ mm_list <- lapply(levels(ex)[cc], function(e) {
if (verbose) {
cat(sprintf("Matching subgroup %s/%s: %s...\n",
- match(e, levels(ex)), nlevels(ex), e))
+ match(e, levels(ex)[cc]), length(cc), e))
}
.e <- which(ex == e)
+ .e1 <- which(ex[treat==1] == e)
treat_ <- treat[.e]
discarded_ <- discarded[.e]
if (!is.null(distance)) distance_ <- distance[.e]
@@ -503,29 +537,49 @@ matchit2nearest <- function(treat, data, distance, discarded,
if (!is.null(mahcovs)) mahcovs_ <- mahcovs[.e,,drop = FALSE]
if (!is.null(antiexactcovs)) antiexactcovs_ <- antiexactcovs[.e,,drop = FALSE]
if (!is.null(distance_mat)) {
- .e1 <- which(ex[treat==1] == e)
.e0 <- which(ex[treat==0] == e)
distance_mat_ <- distance_mat[.e1, .e0, drop = FALSE]
}
- ratio_ <- ratio[ex[treat==1]==e]
+ ratio_ <- ratio[.e1]
n1_ <- sum(treat_ == 1)
- ord_ <- switch(m.order,
- "largest" = order(distance_[treat_ == 1], decreasing = TRUE),
- "smallest" = order(distance_[treat_ == 1], decreasing = FALSE),
- "random" = sample.int(n1_),
- "data" = seq_len(n1_))
+
+
+ if (m.order == "closest") {
+ if (!is.null(mahcovs)) {
+ distance_mat_ <- eucdist_internal(mahcovs_, treat_)
+ }
+ else if (is.null(distance_mat)) {
+ distance_mat_ <- eucdist_internal(distance_, treat_)
+ }
+
+ ord <- NULL
+ }
+ else {
+ ord_ <- switch(m.order,
+ "largest" = order(distance_[treat_ == 1], decreasing = TRUE),
+ "smallest" = order(distance_[treat_ == 1], decreasing = FALSE),
+ "random" = sample.int(n1_),
+ "data" = seq_len(n1_))
+ }
mm_ <- nn_matchC_dispatch(treat_, ord_, ratio_, discarded_, reuse.max, distance_, distance_mat_,
NULL, caliper.dist, caliper.covs, caliper.covs.mat_, mahcovs_,
- antiexactcovs_, NULL, verbose)
+ antiexactcovs_, NULL, m.order, verbose)
#Ensure matched indices correspond to indices in full sample, not subgroup
mm_[] <- seq_along(treat)[.e][mm_]
mm_
})
- mm <- do.call("rbind", mm_list)[lab1,, drop = FALSE]
+ #Construct match.matrix
+ mm <- matrix(NA_integer_, nrow = length(lab1),
+ ncol = max(vapply(mm_list, ncol, numeric(1L))),
+ dimnames = list(lab1, NULL))
+
+ for (m in mm_list) {
+ mm[rownames(m), seq_len(ncol(m))] <- m
+ }
}
if (verbose) cat("Calculating matching weights... ")
@@ -539,21 +593,26 @@ matchit2nearest <- function(treat, data, distance, discarded,
res <- list(match.matrix = nummm2charmm(mm, treat),
subclass = psclass,
- weights = weights.matrix(mm, treat))
+ weights = get_weights_from_mm(mm, treat))
if (verbose) cat("Done.\n")
class(res) <- "matchit"
- return(res)
+ res
}
# Dispatches Rcpp function for NN matching
# nn_matchC_vec() if distance_mat and mahcovs are NULL
# nn_matchC() otherwise
nn_matchC_dispatch <- function(treat, ord, ratio, discarded, reuse.max, distance, distance_mat, ex, caliper.dist,
- caliper.covs, caliper.covs.mat, mahcovs, antiexactcovs, unit.id, verbose) {
- if (is.null(distance_mat) && is.null(mahcovs)) {
+ caliper.covs, caliper.covs.mat, mahcovs, antiexactcovs, unit.id, m.order, verbose) {
+ if (m.order == "closest") {
+ nn_matchC_closest(distance_mat, treat, ratio, discarded, reuse.max,
+ ex, caliper.dist, caliper.covs, caliper.covs.mat,
+ antiexactcovs, unit.id, verbose)
+ }
+ else if (is.null(distance_mat) && is.null(mahcovs)) {
nn_matchC_vec(treat, ord, ratio, discarded, reuse.max, distance,
ex, caliper.dist, caliper.covs, caliper.covs.mat,
antiexactcovs, unit.id, verbose)
@@ -563,4 +622,4 @@ nn_matchC_dispatch <- function(treat, ord, ratio, discarded, reuse.max, distance
ex, caliper.dist, caliper.covs, caliper.covs.mat, mahcovs,
antiexactcovs, unit.id, verbose)
}
-}
\ No newline at end of file
+}
diff --git a/R/matchit2optimal.R b/R/matchit2optimal.R
index 1b6eaf2..5cc05a8 100644
--- a/R/matchit2optimal.R
+++ b/R/matchit2optimal.R
@@ -246,7 +246,7 @@ matchit2optimal <- function(treat, formula, data, distance, discarded,
estimand = "ATT", verbose = FALSE,
is.full.mahalanobis, antiexact = NULL, ...) {
- check.package("optmatch")
+ rlang::check_installed("optmatch")
if (verbose) cat("Optimal matching... \n")
@@ -276,14 +276,14 @@ matchit2optimal <- function(treat, formula, data, distance, discarded,
if (is.full.mahalanobis) {
if (length(attr(terms(formula, data = data), "term.labels")) == 0) {
- stop(sprintf("Covariates must be specified in the input formula when distance = \"%s\".",
- attr(is.full.mahalanobis, "transform")), call. = FALSE)
+ .err(sprintf("covariates must be specified in the input formula when `distance = \"%s\"`",
+ attr(is.full.mahalanobis, "transform")))
}
mahvars <- formula
}
if (!is.null(caliper)) {
- warning("Calipers are currently not compatible with method = \"optimal\" and will be ignored.", call. = FALSE, immediate. = TRUE)
+ .wrn("calipers are currently not compatible with `method = \"optimal\"` and will be ignored")
caliper <- NULL
}
@@ -299,39 +299,41 @@ matchit2optimal <- function(treat, formula, data, distance, discarded,
ex <- factor(exactify(model.frame(exact, data = data),
sep = ", ", include_vars = TRUE)[!discarded])
- cc <- intersect(ex[treat_==1], ex[treat_==0])
- if (length(cc) == 0) stop("No matches were found.", call. = FALSE)
+ cc <- intersect(as.integer(ex)[treat_==1], as.integer(ex)[treat_==0])
+ if (length(cc) == 0) .err("No matches were found")
e_ratios <- vapply(levels(ex), function(e) sum(treat_[ex == e] == 0)/sum(treat_[ex == e] == 1), numeric(1L))
if (any(e_ratios < 1)) {
- warning(sprintf("Fewer %s units than %s units in some 'exact' strata; not all %s units will get a match.",
- tc[2], tc[1], tc[1]), immediate. = TRUE, call. = FALSE)
+ .wrn(sprintf("Fewer %s units than %s units in some `exact` strata; not all %s units will get a match",
+ tc[2], tc[1], tc[1]))
}
if (ratio > 1 && any(e_ratios < ratio)) {
if (ratio == max.controls)
- warning(sprintf("Not all %s units will get %s matches.",
- tc[1], ratio), immediate. = TRUE, call. = FALSE)
+ .wrn(sprintf("Not all %s units will get %s matches",
+ tc[1], ratio))
else
- warning(sprintf("Not enough %s units for an average of %s matches per %s unit in all 'exact' strata.",
- tc[2], ratio, tc[1]), immediate. = TRUE, call. = FALSE)
+ .wrn(sprintf("Not enough %s units for an average of %s matches per %s unit in all `exact` strata",
+ tc[2], ratio, tc[1]))
}
}
else {
ex <- factor(rep("_", length(treat_)), levels = "_")
+ cc <- 1
+
e_ratios <- setNames(sum(treat_ == 0)/sum(treat_ == 1), levels(ex))
if (e_ratios < 1) {
- warning(sprintf("Fewer %s units than %s units; not all %s units will get a match.",
- tc[2], tc[1], tc[1]), immediate. = TRUE, call. = FALSE)
+ .wrn(sprintf("Fewer %s units than %s units; not all %s units will get a match",
+ tc[2], tc[1], tc[1]))
}
else if (e_ratios < ratio) {
if (ratio == max.controls)
- warning(sprintf("Not all %s units will get %s matches.",
- tc[1], ratio), immediate. = TRUE, call. = FALSE)
+ .wrn(sprintf("Not all %s units will get %s matches",
+ tc[1], ratio))
else
- warning(sprintf("Not enough %s units for an average of %s matches per %s unit.",
- tc[2], ratio, tc[1]), immediate. = TRUE, call. = FALSE)
+ .wrn(sprintf("Not enough %s units for an average of %s matches per %s unit",
+ tc[2], ratio, tc[1]))
}
}
@@ -348,7 +350,7 @@ matchit2optimal <- function(treat, formula, data, distance, discarded,
mo <- distance
}
else {
- mo <- eucdist_internal(setNames(distance, names(treat)), treat)
+ mo <- eucdist_internal(setNames(as.numeric(distance), names(treat)), treat)
}
#Transpose distance mat as needed
@@ -375,14 +377,18 @@ matchit2optimal <- function(treat, formula, data, distance, discarded,
t_df <- data.frame(treat)
- for (e in levels(ex)) {
+ for (e in levels(ex)[cc]) {
if (nlevels(ex) > 1) {
+ if (verbose) {
+ cat(sprintf("Matching subgroup %s/%s: %s...\n",
+ match(e, levels(ex)[cc]), length(cc), e))
+ }
mo_ <- mo[ex[treat_==1] == e, ex[treat_==0] == e]
}
else mo_ <- mo
- if (any(dim(mo_) == 0)) next
- else if (all(dim(mo_) == 1)) {
+ if (any(dim(mo_) == 0) || !any(is.finite(mo_))) next
+ else if (all(dim(mo_) == 1) && all(is.finite(mo_))) {
pair[ex == e] <- paste(1, e, sep = "|")
next
}
@@ -406,27 +412,20 @@ matchit2optimal <- function(treat, formula, data, distance, discarded,
max.controls_ <- max.controls
}
- withCallingHandlers({
+ matchit_try({
p[[e]] <- do.call(optmatch::fullmatch,
c(list(mo_,
mean.controls = ratio_,
min.controls = min.controls_,
max.controls = max.controls_,
- data = t_df), #just to get rownames; not actually used in matching
+ data = t_df[ex == e,, drop = FALSE]), #just to get rownames; not actually used in matching
A))
- },
- warning = function(w) {
- warning(paste0("(from optmatch) ", conditionMessage(w)), call. = FALSE, immediate. = TRUE)
- invokeRestart("muffleWarning")
- },
- error = function(e1) {
- stop(paste0("(from optmatch) ", conditionMessage(e1)), call. = FALSE)
- })
+ }, from = "optmatch")
pair[names(p[[e]])[!is.na(p[[e]])]] <- paste(as.character(p[[e]][!is.na(p[[e]])]), e, sep = "|")
}
- if (all(is.na(pair))) stop("No matches were found.", call. = FALSE)
+ if (all(is.na(pair))) .err("No matches were found")
if (length(p) == 1) p <- p[[1]]
psclass <- factor(pair)
@@ -440,11 +439,11 @@ matchit2optimal <- function(treat, formula, data, distance, discarded,
## calculate weights and return the results
res <- list(match.matrix = mm,
subclass = psclass,
- weights = weights.subclass(psclass, treat, estimand),
+ weights = get_weights_from_subclass(psclass, treat, estimand),
obj = p)
if (verbose) cat("Done.\n")
class(res) <- "matchit"
- return(res)
+ res
}
diff --git a/R/matchit2quick.R b/R/matchit2quick.R
index a99959b..cd31097 100644
--- a/R/matchit2quick.R
+++ b/R/matchit2quick.R
@@ -8,7 +8,7 @@
#' matching, which is a form of subclassification wherein all units, both
#' treatment and control (i.e., the "full" sample), are assigned to a subclass
#' and receive at least one match. It uses an algorithm that is extremely fast
-#' compared to optimal full matching, which is why it is labelled as "quick", at the
+#' compared to optimal full matching, which is why it is labeled as "quick", at the
#' expense of true optimality. The method is described in Sävje, Higgins, & Sekhon (2021). The method relies on and is a wrapper
#' for \pkgfun{quickmatch}{quickmatch}.
#'
@@ -141,7 +141,7 @@ matchit2quick <- function(treat, formula, data, distance, discarded,
estimand = "ATT", verbose = FALSE,
is.full.mahalanobis, ...) {
- check.package("quickmatch")
+ rlang::check_installed("quickmatch")
if (verbose) cat("Generalized full matching... \n")
@@ -166,8 +166,8 @@ matchit2quick <- function(treat, formula, data, distance, discarded,
if (is.full.mahalanobis) {
if (length(attr(terms(formula, data = data), "term.labels")) == 0) {
- stop(sprintf("Covariates must be specified in the input formula when distance = \"%s\".",
- attr(is.full.mahalanobis, "transform")), call. = FALSE)
+ .err(sprintf("covariates must be specified in the input formula when `distance = \"%s\"`",
+ attr(is.full.mahalanobis, "transform")))
}
mahvars <- formula
}
@@ -176,11 +176,13 @@ matchit2quick <- function(treat, formula, data, distance, discarded,
if (!is.null(exact)) {
ex <- factor(exactify(model.frame(exact, data = data),
sep = ", ", include_vars = TRUE)[!discarded])
- cc <- intersect(ex[treat_==1], ex[treat_==0])
- if (length(cc) == 0) stop("No matches were found.", call. = FALSE)
+ cc <- intersect(as.integer(ex)[treat_==1], as.integer(ex)[treat_==0])
+ if (length(cc) == 0) .err("no matches were found")
+
}
else {
ex <- factor(rep("_", length(treat_)), levels = "_")
+ cc <- 1
}
#Create distance matrix; note that Mahalanobis distance computed using entire
@@ -195,7 +197,6 @@ matchit2quick <- function(treat, formula, data, distance, discarded,
distcovs <- as.matrix(distance)
}
-
#Remove discarded units from distance mat
distcovs <- distcovs[!discarded, , drop = FALSE]
rownames(distcovs) <- names(treat_)
@@ -203,10 +204,10 @@ matchit2quick <- function(treat, formula, data, distance, discarded,
#Process caliper
if (!is.null(caliper)) {
if (!is.null(mahvars)) {
- stop("With method = \"quick\", a caliper can only be used when 'distance' is a propensity score or vector and 'mahvars' is not specified.", call. = FALSE)
+ .err("with `method = \"quick\"`, a caliper can only be used when `distance` is a propensity score or vector and `mahvars` is not specified")
}
if (length(caliper) > 1 || !identical(names(caliper), "")) {
- stop("With method = \"quick\", calipers cannot be placed on covariates.", call. = FALSE)
+ .err("with `method = \"quick\"`, calipers cannot be placed on covariates")
}
}
@@ -214,33 +215,26 @@ matchit2quick <- function(treat, formula, data, distance, discarded,
pair <- setNames(rep(NA_character_, length(treat)), names(treat))
p <- setNames(vector("list", nlevels(ex)), levels(ex))
- for (e in levels(ex)) {
- if (nlevels(ex) > 1) {
- distcovs_ <- distcovs[ex == e,, drop = FALSE]
+ for (e in levels(ex)[cc]) {
+ if (verbose && nlevels(ex) > 1) {
+ cat(sprintf("Matching subgroup %s/%s: %s...\n",
+ match(e, levels(ex)[cc]), length(cc), e))
}
- else distcovs_ <- distcovs
- if (nrow(distcovs) == 0) next
+ distcovs_ <- distcovs[ex == e,, drop = FALSE]
- withCallingHandlers({
+ matchit_try({
p[[e]] <- do.call(quickmatch::quickmatch,
- c(list(distcovs,
- treatments = treat_,
+ c(list(distcovs_,
+ treatments = treat_[ex == e],
caliper = caliper),
A))
- },
- warning = function(w) {
- warning(paste0("(from quickmatch) ", conditionMessage(w)), call. = FALSE, immediate. = TRUE)
- invokeRestart("muffleWarning")
- },
- error = function(e1) {
- stop(paste0("(from quickmatch) ", conditionMessage(e1)), call. = FALSE)
- })
+ }, from = "quickmatch")
pair[which(ex == e)[!is.na(p[[e]])]] <- paste(as.character(p[[e]][!is.na(p[[e]])]), e, sep = "|")
}
- if (all(is.na(pair))) stop("No matches were found.", call. = FALSE)
+ if (all(is.na(pair))) .err("no matches were found")
if (length(p) == 1) p <- p[[1]]
psclass <- factor(pair)
@@ -253,11 +247,11 @@ matchit2quick <- function(treat, formula, data, distance, discarded,
if (verbose) cat("Calculating matching weights... ")
res <- list(subclass = psclass,
- weights = weights.subclass(psclass, treat, estimand),
+ weights = get_weights_from_subclass(psclass, treat, estimand),
obj = p)
if (verbose) cat("Done.\n")
class(res) <- c("matchit")
- return(res)
-}
\ No newline at end of file
+ res
+}
diff --git a/R/matchit2subclass.R b/R/matchit2subclass.R
index a86ad1f..aca5a79 100644
--- a/R/matchit2subclass.R
+++ b/R/matchit2subclass.R
@@ -170,31 +170,24 @@ matchit2subclass <- function(treat, distance, discarded,
#Checks
if (is.null(subclass)) subclass <- 6
- else if (!is.numeric(subclass) || !is.null(dim(subclass))) {
- stop("`subclass` must be a numeric value.", call. = FALSE)
- }
- else if (length(subclass) == 1) {
- if (round(subclass) <= 1) {
- stop("`subclass` must be greater than 1.",call.=FALSE)
- }
+ chk::chk_numeric(subclass)
+
+ if (length(subclass) == 1) {
+ chk::chk_gt(subclass, 1)
}
else if (!all(subclass <= 1 & subclass >= 0)) {
- stop("When specifying `subclass` as a vector of quantiles, all values must be between 0 and 1.",
- call. = FALSE)
+ .err("When specifying `subclass` as a vector of quantiles, all values must be between 0 and 1")
}
if (!is.null(sub.by)) {
- stop("`sub.by` is defunct and has been replaced with `estimand`.", call. = FALSE)
- }
- else {
- estimand <- toupper(estimand)
- estimand <- match_arg(estimand, c("ATT", "ATC", "ATE"))
+ .err("`sub.by` is defunct and has been replaced with `estimand`")
}
+ estimand <- toupper(estimand)
+ estimand <- match_arg(estimand, c("ATT", "ATC", "ATE"))
+
if (is.null(min.n)) min.n <- 1
- else if (!is.numeric(min.n) || length(min.n) != 1) {
- stop("`min.n` must be a single number.", call. = FALSE)
- }
+ chk::chk_count(min.n)
n.obs <- length(treat)
@@ -219,7 +212,7 @@ matchit2subclass <- function(treat, distance, discarded,
psclass[!discarded] <- as.integer(findInterval(distance[!discarded], q, all.inside = TRUE))
if (length(unique(na.omit(psclass))) != subclass){
- warning("Due to discreteness in the distance measure, fewer subclasses were generated than were requested.", call.=FALSE)
+ .wrn("Due to discreteness in the distance measure, fewer subclasses were generated than were requested")
}
if (min.n == 0) {
@@ -240,10 +233,10 @@ matchit2subclass <- function(treat, distance, discarded,
if (verbose) cat("Calculating matching weights... ")
res <- list(subclass = psclass, q.cut = q,
- weights = weights.subclass(psclass, treat, estimand))
+ weights = get_weights_from_subclass(psclass, treat, estimand))
if (verbose) cat("Done.\n")
class(res) <- c("matchit.subclass", "matchit")
- return(res)
+ res
}
diff --git a/R/plot.matchit.R b/R/plot.matchit.R
index d12ee93..84cd543 100644
--- a/R/plot.matchit.R
+++ b/R/plot.matchit.R
@@ -54,7 +54,7 @@
#' variable with fewer than 5 unique values, points are jittered to more easily
#' visualize counts.
#'
-#' With `type = "ecdf"`, empirical cumulative density function (eCDF)
+#' With `type = "ecdf"`, empirical cumulative distribution function (eCDF)
#' plots are created for each covariate before and after matching. Two eCDF
#' lines are produced in each plot: a gray one for control units and a black
#' one for treated units. Each point on the lines corresponds to the proportion
@@ -140,6 +140,7 @@
#' @exportS3Method plot matchit
plot.matchit <- function(x, type = "qq", interactive = TRUE, which.xs = NULL, data = NULL, ...) {
+ chk::chk_string(type)
type <- tolower(type)
type <- match_arg(type, c("qq", "ecdf", "density", "jitter", "histogram"))
@@ -149,13 +150,13 @@ plot.matchit <- function(x, type = "qq", interactive = TRUE, which.xs = NULL, da
}
else if (type == "jitter") {
if (is.null(x$distance)) {
- stop("type = \"jitter\" cannot be used if a distance measure is not estimated or supplied. No plots generated.", call. = FALSE)
+ .err("`type = \"jitter\"` cannot be used if a distance measure is not estimated or supplied. No plots generated")
}
jitter.pscore(x, interactive = interactive,...)
}
else if (type == "histogram") {
if (is.null(x$distance)) {
- stop("type = \"hist\" cannot be used if a distance measure is not estimated or supplied. No plots generated.", call. = FALSE)
+ .err("`type = \"hist\"` cannot be used if a distance measure is not estimated or supplied. No plots generated")
}
hist.pscore(x,...)
}
@@ -177,9 +178,11 @@ plot.matchit.subclass <- function(x, type = "qq", interactive = TRUE, which.xs =
print.data.frame(Choices, right=FALSE)
ans <- readline(question)
}
- return(ans)
+
+ ans
}
+ chk::chk_string(type)
type <- tolower(type)
type <- match_arg(type, c("qq", "ecdf", "density", "jitter", "histogram"))
@@ -193,8 +196,7 @@ plot.matchit.subclass <- function(x, type = "qq", interactive = TRUE, which.xs =
if (miss.sub || isFALSE(subclass)) which.subclass <- NULL
else if (isTRUE(subclass)) which.subclass <- subclasses
else if (!is.atomic(subclass) || !all(subclass %in% seq_along(subclasses))) {
- stop("'subclass' should be TRUE, FALSE, or a vector of subclass indices for which subclass balance is to be displayed.",
- call. = FALSE)
+ .err("`subclass` should be `TRUE`, `FALSE`, or a vector of subclass indices for which subclass balance is to be displayed")
}
else which.subclass <- subclasses[subclass]
@@ -206,9 +208,9 @@ plot.matchit.subclass <- function(x, type = "qq", interactive = TRUE, which.xs =
subclasses <- levels(x$subclass)
choices <- c("No (Exit)", paste0("Yes: Subclass ", subclasses), "Yes: In aggregate")
plot.name <- switch(type, "qq" = "quantile-quantile", "ecdf" = "empirical CDF", "density" = "density")
- question <- paste("Would you like to see", plot.name, "plots of any subclasses? ")
+ question <- sprintf("Would you like to see %s plots of any subclasses? ", plot.name)
ans <- -1
- while(ans != 0) {
+ while (ans != 0) {
ans <- as.numeric(choice.menu(choices, question))
if (ans %in% seq_along(subclasses) && any(x$subclass == subclasses[ans])) {
matchit.covplot.subclass(x, type = type, which.subclass = subclasses[ans],
@@ -225,13 +227,13 @@ plot.matchit.subclass <- function(x, type = "qq", interactive = TRUE, which.xs =
}
else if (type=="jitter") {
if (is.null(x$distance)) {
- stop("type = \"jitter\" cannot be used when no distance variable was estimated or supplied.", call. = FALSE)
+ .err("`type = \"jitter\"` cannot be used when no distance variable was estimated or supplied")
}
jitter.pscore(x, interactive = interactive, ...)
}
else if (type == "histogram") {
if (is.null(x$distance)) {
- stop("type = \"histogram\" cannot be used when no distance variable was estimated or supplied.", call. = FALSE)
+ .err("`type = \"histogram\"` cannot be used when no distance variable was estimated or supplied")
}
hist.pscore(x,...)
}
@@ -243,7 +245,7 @@ matchit.covplot <- function(object, type = "qq", interactive = TRUE, which.xs =
if (is.null(which.xs)) {
if (length(object$X) == 0) {
- warning("No covariates to plot.", call. = FALSE)
+ .wrn("No covariates to plot")
return(invisible(NULL))
}
X <- object$X
@@ -261,8 +263,7 @@ matchit.covplot <- function(object, type = "qq", interactive = TRUE, which.xs =
else {
if (!is.null(data)) {
if (!is.data.frame(data) || nrow(data) != length(object$treat)) {
- stop("'data' must be a data frame with as many rows as there are units in the supplied 'matchit' object.",
- call. = FALSE)
+ .err("`data` must be a data frame with as many rows as there are units in the supplied `matchit` object")
}
data <- cbind(data, object$X[setdiff(names(object$X), names(data))])
}
@@ -282,29 +283,37 @@ matchit.covplot <- function(object, type = "qq", interactive = TRUE, which.xs =
if (is.character(which.xs)) {
if (!all(which.xs %in% names(data))) {
- stop("All variables in 'which.xs' must be in the supplied 'matchit' object or in 'data'.",
- call. = FALSE)
+ .err("All variables in `which.xs` must be in the supplied `matchit` object or in `data`")
}
X <- data[which.xs]
}
- else if (inherits(which.xs, "formula")) {
- which.xs <- update(which.xs, NULL ~ .)
+ else if (rlang::is_formula(which.xs)) {
+ which.xs <- update(terms(which.xs, data = data), NULL ~ .)
X <- model.frame(which.xs, data, na.action = "na.pass")
-
- if (anyNA(X)) {
- stop("Missing values are not allowed in the covariates named in 'which.xs'.",
- call. = FALSE)
- }
}
else {
- stop("'which.xs' must be supplied as a character vector of names or a one-sided formula.",
- call. = FALSE)
+ .err("`which.xs` must be supplied as a character vector of names or a one-sided formula")
+ }
+
+ # if (anyNA(X)) {
+ # stop("Missing values are not allowed in the covariates named in `which.xs`.",
+ # call. = FALSE)
+ # }
+
+ k <- ncol(X)
+ for (i in seq_len(k)) {
+ if (anyNA(X[[i]]) || (is.numeric(X[[i]]) && any(!is.finite(X[[i]])))) {
+ covariates.with.missingness <- names(X)[i:k][vapply(i:k, function(j) anyNA(X[[j]]) ||
+ (is.numeric(X[[j]]) &&
+ any(!is.finite(X[[j]]))),
+ logical(1L))]
+ .err(paste0("Missing and non-finite values are not allowed in the covariates named in `which.xs`. Variables with missingness or non-finite values:\n\t",
+ paste(covariates.with.missingness, collapse = ", ")), tidy = FALSE)
+ }
+ if (is.character(X[[i]])) X[[i]] <- factor(X[[i]])
}
}
- chars.in.X <- vapply(X, is.character, logical(1L))
- X[chars.in.X] <- lapply(X[chars.in.X], factor)
-
X <- droplevels(X)
t <- object$treat
@@ -313,8 +322,8 @@ matchit.covplot <- function(object, type = "qq", interactive = TRUE, which.xs =
w <- object$weights * sw
if (is.null(w)) w <- rep(1, length(t))
- split(w, t) <- lapply(split(w, t), function(x) x/sum(x))
- split(sw, t) <- lapply(split(sw, t), function(x) x/sum(x))
+ w <- .make_sum_to_1(w, by = t)
+ sw <- .make_sum_to_1(sw, by = t)
if (type == "density") {
varnames <- names(X)
@@ -384,7 +393,7 @@ matchit.covplot <- function(object, type = "qq", interactive = TRUE, which.xs =
}
devAskNewPage(ask = FALSE)
- return(invisible(NULL))
+ invisible(NULL)
}
matchit.covplot.subclass <- function(object, type = "qq", which.subclass = NULL,
@@ -392,7 +401,7 @@ matchit.covplot.subclass <- function(object, type = "qq", which.subclass = NULL,
if (is.null(which.xs)) {
if (length(object$X) == 0) {
- warning("No covariates to plot.", call. = FALSE)
+ .wrn("No covariates to plot")
return(invisible(NULL))
}
X <- object$X
@@ -410,8 +419,7 @@ matchit.covplot.subclass <- function(object, type = "qq", which.subclass = NULL,
else {
if (!is.null(data)) {
if (!is.data.frame(data) || nrow(data) != length(object$treat)) {
- stop("'data' must be a data frame with as many rows as there are units in the supplied 'matchit' object.",
- call. = FALSE)
+ .err("`data` must be a data frame with as many rows as there are units in the supplied `matchit` object")
}
data <- cbind(data, object$X[setdiff(names(object$X), names(data))])
}
@@ -431,23 +439,20 @@ matchit.covplot.subclass <- function(object, type = "qq", which.subclass = NULL,
if (is.character(which.xs)) {
if (!all(which.xs %in% names(data))) {
- stop("All variables in 'which.xs' must be in the supplied 'matchit' object or in 'data'.",
- call. = FALSE)
+ .err("All variables in `which.xs` must be in the supplied `matchit` object or in `data`")
}
X <- data[which.xs]
}
- else if (inherits(which.xs, "formula")) {
- which.xs <- update(which.xs, NULL ~ .)
+ else if (rlang::is_formula(which.xs)) {
+ which.xs <- update(terms(which.xs, data = data), NULL ~ .)
X <- model.frame(which.xs, data, na.action = "na.pass")
if (anyNA(X)) {
- stop("Missing values are not allowed in the covariates named in 'which.xs'.",
- call. = FALSE)
+ .err("Missing values are not allowed in the covariates named in `which.xs`")
}
}
else {
- stop("'which.xs' must be supplied as a character vector of names or a one-sided formula.",
- call. = FALSE)
+ .err("`which.xs` must be supplied as a character vector of names or a one-sided formula")
}
}
@@ -459,10 +464,10 @@ matchit.covplot.subclass <- function(object, type = "qq", which.subclass = NULL,
t <- object$treat
if (!is.atomic(which.subclass)) {
- stop("The argument to 'subclass' must be NULL or the indices of the subclasses for which to display covariate distributions.", call. = FALSE)
+ .err("The argument to `subclass` must be NULL or the indices of the subclasses for which to display covariate distributions")
}
if (!all(which.subclass %in% object$subclass[!is.na(object$subclass)])) {
- stop("The argument supplied to 'subclass' is not the index of any subclass in the matchit object.", call. = FALSE)
+ .err("The argument supplied to `subclass` is not the index of any subclass in the matchit object")
}
if (type == "density") {
@@ -489,8 +494,8 @@ matchit.covplot.subclass <- function(object, type = "qq", which.subclass = NULL,
sw <- if (is.null(object$s.weights)) rep(1, length(t)) else object$s.weights
w <- sw*(!is.na(object$subclass) & object$subclass == s)
- split(w, t) <- lapply(split(w, t), function(x) x/sum(x))
- split(sw, t) <- lapply(split(sw, t), function(x) x/sum(x))
+ w <- .make_sum_to_1(w, by = t)
+ sw <- .make_sum_to_1(sw, by = t)
for (i in seq_along(varnames)){
@@ -544,7 +549,7 @@ matchit.covplot.subclass <- function(object, type = "qq", which.subclass = NULL,
}
devAskNewPage(ask = FALSE)
- return(invisible(NULL))
+ invisible(NULL)
}
qqplot_match <- function(x, t, w, sw, discrete.cutoff = 5, ...) {
@@ -565,36 +570,39 @@ qqplot_match <- function(x, t, w, sw, discrete.cutoff = 5, ...) {
x1 <- x_ord[t_ord == 1][sw1 > 0]
x0 <- x_ord[t_ord != 1][sw0 > 0]
- swn1 <- sum(sw[t==1] > 0)
- swn0 <- sum(sw[t==0] > 0)
+ sw1 <- sw1[sw1 > 0]
+ sw0 <- sw0[sw0 > 0]
+
+ swn1 <- length(sw1)
+ swn0 <- length(sw0)
if (swn1 < swn0) {
if (length(u) <= discrete.cutoff) {
- x0probs <- vapply(u, function(u_) wm(x0 == u_, sw0[sw0 > 0]), numeric(1L))
- x0cumprobs <- c(0, cumsum(x0probs)[-length(u)], 1)
- x0 <- u[findInterval(cumsum(sw1[sw1 > 0]), x0cumprobs, rightmost.closed = TRUE)]
+ x0probs <- vapply(u, function(u_) wm(x0 == u_, sw0), numeric(1L))
+ x0cumprobs <- c(0, .cumsum_prob(x0probs))
+ x0 <- u[findInterval(.cumsum_prob(sw1), x0cumprobs, rightmost.closed = TRUE)]
}
else {
- x0 <- approx(cumsum(sw0[sw0 > 0]), y = x0, xout = cumsum(sw1[sw1 > 0]), rule = 2,
+ x0 <- approx(.cumsum_prob(sw0), y = x0, xout = .cumsum_prob(sw1), rule = 2,
method = "constant", ties = "ordered")$y
}
}
- else {
+ else if (swn1 > swn0) {
if (length(u) <= discrete.cutoff) {
- x1probs <- vapply(u, function(u_) wm(x1 == u_, sw1[sw1 > 0]), numeric(1L))
- x1cumprobs <- c(0, cumsum(x1probs)[-length(u)], 1)
- x1 <- u[findInterval(cumsum(sw0[sw0 > 0]), x1cumprobs, rightmost.closed = TRUE)]
+ x1probs <- vapply(u, function(u_) wm(x1 == u_, sw1), numeric(1L))
+ x1cumprobs <- c(0, .cumsum_prob(x1probs))
+ x1 <- u[findInterval(.cumsum_prob(sw0), x1cumprobs, rightmost.closed = TRUE)]
}
else {
- x1 <- approx(cumsum(sw1[sw1 > 0]), y = x1, xout = cumsum(sw0[sw0 > 0]), rule = 2,
+ x1 <- approx(.cumsum_prob(sw1), y = x1, xout = .cumsum_prob(sw0), rule = 2,
method = "constant", ties = "ordered")$y
}
}
if (length(u) <= discrete.cutoff) {
md <- min(diff(u))
- x0 <- jitter(x0, amount = .1*md)
- x1 <- jitter(x1, amount = .1*md)
+ x0 <- jitter(x0, amount = .1 * md)
+ x1 <- jitter(x1, amount = .1 * md)
}
rr <- range(c(x0, x1))
@@ -613,36 +621,39 @@ qqplot_match <- function(x, t, w, sw, discrete.cutoff = 5, ...) {
x1 <- x_ord[t_ord == 1][w1 > 0]
x0 <- x_ord[t_ord != 1][w0 > 0]
- wn1 <- sum(w[t==1] > 0)
- wn0 <- sum(w[t==0] > 0)
+ w1 <- w1[w1 > 0]
+ w0 <- w0[w0 > 0]
+
+ wn1 <- length(w1)
+ wn0 <- length(w0)
if (wn1 < wn0) {
if (length(u) <= discrete.cutoff) {
- x0probs <- vapply(u, function(u_) wm(x0 == u_, w0[w0 > 0]), numeric(1L))
- x0cumprobs <- c(0, cumsum(x0probs)[-length(u)], 1)
- x0 <- u[findInterval(cumsum(w1[w1 > 0]), x0cumprobs, rightmost.closed = TRUE)]
+ x0probs <- vapply(u, function(u_) wm(x0 == u_, w0), numeric(1L))
+ x0cumprobs <- c(0, .cumsum_prob(x0probs))
+ x0 <- u[findInterval(.cumsum_prob(w1), x0cumprobs, rightmost.closed = TRUE)]
}
else {
- x0 <- approx(cumsum(w0[w0 > 0]), y = x0, xout = cumsum(w1[w1 > 0]), rule = 2,
+ x0 <- approx(.cumsum_prob(w0), y = x0, xout = .cumsum_prob(w1), rule = 2,
method = "constant", ties = "ordered")$y
}
}
- else {
+ else if (wn1 > wn0) {
if (length(u) <= discrete.cutoff) {
- x1probs <- vapply(u, function(u_) wm(x1 == u_, w1[w1 > 0]), numeric(1L))
- x1cumprobs <- c(0, cumsum(x1probs)[-length(u)], 1)
- x1 <- u[findInterval(cumsum(w0[w0 > 0]), x1cumprobs, rightmost.closed = TRUE)]
+ x1probs <- vapply(u, function(u_) wm(x1 == u_, w1), numeric(1L))
+ x1cumprobs <- c(0, .cumsum_prob(x1probs))
+ x1 <- u[findInterval(.cumsum_prob(w0), x1cumprobs, rightmost.closed = TRUE)]
}
else {
- x1 <- approx(cumsum(w1[w1 > 0]), y = x1, xout = cumsum(w0[w0 > 0]), rule = 2,
+ x1 <- approx(.cumsum_prob(w1), y = x1, xout = .cumsum_prob(w0), rule = 2,
method = "constant", ties = "ordered")$y
}
}
if (length(u) <= discrete.cutoff) {
md <- min(diff(u))
- x0 <- jitter(x0, amount = .1*md)
- x1 <- jitter(x1, amount = .1*md)
+ x0 <- jitter(x0, amount = .1 * md)
+ x1 <- jitter(x1, amount = .1 * md)
}
plot(x0, x1, xlab = "", ylab = "", xlim = rr, ylim = rr, axes = FALSE, ...)
@@ -665,11 +676,10 @@ ecdfplot_match <- function(x, t, w, sw, ...) {
for (tr in 0:1) {
in.tr <- t[ord] == tr
ordt <- ord[in.tr]
- cswt <- c(0, cumsum(sw[ordt]), 1)
+ cswt <- c(0, .cumsum_prob(sw[ordt]), 1)
xt <- c(x.min - .02 * x.range, x[ordt], x.max + .02 * x.range)
lines(x = xt, y = cswt, type = "s", col = if (tr == 0) "grey60" else "black")
-
}
abline(h = 0:1)
@@ -681,7 +691,7 @@ ecdfplot_match <- function(x, t, w, sw, ...) {
for (tr in 0:1) {
in.tr <- t[ord] == tr
ordt <- ord[in.tr]
- cwt <- c(0, cumsum(w[ordt]), 1)
+ cwt <- c(0, .cumsum_prob(w[ordt]), 1)
xt <- c(x.min - .02 * x.range, x[ordt], x.max + .02 * x.range)
lines(x = xt, y = cwt, type = "s", col = if (tr == 0) "grey60" else "black")
@@ -820,12 +830,13 @@ hist.pscore <- function(x, xlab = "Propensity Score", freq = FALSE, ...){
if (is.null(ratio)) ratio <- 1
- for (i in unique(treat, nmax = 2)) {
- if (freq) s.weights[treat == i] <- s.weights[treat == i]/mean(s.weights[treat == i])
- else s.weights[treat == i] <- s.weights[treat == i]/sum(s.weights[treat == i])
-
- if (freq) weights[treat == i] <- weights[treat == i]/mean(weights[treat == i])
- else weights[treat == i] <- weights[treat == i]/sum(weights[treat == i])
+ if (freq) {
+ weights <- .make_sum_to_n(weights, by = treat)
+ s.weights <- .make_sum_to_n(s.weights, by = treat)
+ }
+ else {
+ weights <- .make_sum_to_1(weights, by = treat)
+ s.weights <- .make_sum_to_1(s.weights, by = treat)
}
ylab <- if (freq) "Count" else "Proportion"
@@ -877,8 +888,8 @@ jitter.pscore <- function(x, interactive, pch = 1, ...){
maxp <- max(pscore, na.rm = TRUE)
plot(pscore, xlim = c(minp - 0.05*(maxp-minp), maxp + 0.05*(maxp-minp)), ylim = c(-1.5,2.5),
- type="n", ylab="", xlab="Propensity Score",
- axes=FALSE,main="Distribution of Propensity Scores",...)
+ type = "n", ylab = "", xlab = "Propensity Score",
+ axes = FALSE, main = "Distribution of Propensity Scores", ...)
if (!is.null(q.cut)) abline(v = q.cut, col = "grey", lty = 1)
#Matched treated
@@ -904,7 +915,7 @@ jitter.pscore <- function(x, interactive, pch = 1, ...){
box()
if (interactive) {
- print("To identify the units, use first mouse button; to stop, use second.")
+ cat("To identify the units, use first mouse button; to stop, use second.\n")
identify(pscore, jitp, names(treat), atpen = TRUE)
}
}
diff --git a/R/plot.summary.matchit.R b/R/plot.summary.matchit.R
index 4aba208..6d2e4da 100644
--- a/R/plot.summary.matchit.R
+++ b/R/plot.summary.matchit.R
@@ -91,7 +91,7 @@ plot.summary.matchit <- function(x,
standard.sum <- if (un) x[["sum.all"]] else x[[if (sub) "sum.across" else "sum.matched"]]
if (!"Std. Mean Diff." %in% colnames(standard.sum)) {
- stop("Not appropriate for unstandardized summary. Run summary() with the standardize = TRUE option, and then plot.", call. = FALSE)
+ .err("not appropriate for unstandardized summary. Run `summary()` with the `standardize = TRUE` option, and then plot")
}
if (un) {
@@ -101,12 +101,15 @@ plot.summary.matchit <- function(x,
sd.matched <- x[[if (sub) "sum.across" else "sum.matched"]][,"Std. Mean Diff."]
}
+ chk::chk_flag(abs)
+
var.names <- rownames(standard.sum)
+ chk::chk_string(var.order)
var.order <- match_arg(var.order, c("data", "matched", "unmatched", "alphabetical"))
- if (!un && var.order == "unmatched") stop("'var.order' cannot be \"unmatched\" if un = TRUE in the call to summary().", call. = FALSE)
- if (!matched && var.order == "matched") stop("'var.order' cannot be \"matched\" if method = NULL in the original call to matchit().", call. = FALSE)
+ if (!un && var.order == "unmatched") .err("`var.order` cannot be \"unmatched\" if `un = TRUE` in the call to `summary()`")
+ if (!matched && var.order == "matched") .err("`var.order` cannot be \"matched\" if `method = NULL` in the original call to `matchit()`")
if (abs) {
if (un) sd.all <- abs(sd.all)
diff --git a/R/rbind.matchdata.R b/R/rbind.matchdata.R
index 6ad2989..9089155 100644
--- a/R/rbind.matchdata.R
+++ b/R/rbind.matchdata.R
@@ -84,8 +84,8 @@ rbind.matchdata <- function(..., deparse.level = 1) {
allargs$deparse.level <- deparse.level
type <- intersect(c("matchdata", "getmatches"), unlist(lapply(md_list, class)))
- if (length(type) == 0) stop("A matchdata or getmatches object must be supplied.", call. = FALSE)
- else if (length(type) == 2) stop("Supplied objects must be all matchdata objects or all getmatches objects.", call. = FALSE)
+ if (length(type) == 0) .err("A `matchdata` or `getmatches` object must be supplied")
+ if (length(type) == 2) .err("Supplied objects must be all `matchdata` objects or all `getmatches` objects")
attrs <- c("distance", "weights", "subclass", "id")
attr_list <- setNames(vector("list", length(attrs)), attrs)
@@ -108,7 +108,8 @@ rbind.matchdata <- function(..., deparse.level = 1) {
})
for (d in seq_along(md_list)[-1]) {
if (length(other_col_list[[d]]) != length(other_col_list[[1]]) || !all(other_col_list[[d]] %in% other_col_list[[1]])) {
- stop(paste("The", switch(type, "matchdata" = "match.data()", "get_matches()"), "inputs must come from the same dataset."), call. = FALSE)
+ .err(sprintf("the %s inputs must come from the same dataset",
+ switch(type, "matchdata" = "`match.data()`", "`get_matches()`")))
}
}
diff --git a/R/summary.matchit.R b/R/summary.matchit.R
index 22c8205..590a4a8 100644
--- a/R/summary.matchit.R
+++ b/R/summary.matchit.R
@@ -204,50 +204,7 @@ summary.matchit <- function(object,
...) {
#Create covariate matrix; include caliper, exact, and mahvars
-
- if (is.null(object$X)) {
- X <- matrix(nrow = length(object$treat), ncol = 0)
- }
- else {
- X <- get.covs.matrix(data = object$X)
- }
- X_assign <- get_assign(X)
-
- if (!is.null(addlvariables)) {
- if (is.character(addlvariables)) {
- if (!is.null(data) && is.data.frame(data)) {
- if (all(addlvariables %in% names(data))) {
- addlvariables <- data[addlvariables]
- }
- else {
- stop("All variables in 'addlvariables' must be in 'data'.", call. = FALSE)
- }
- }
- else {
- stop("If 'addlvariables' is specified as a string, a data frame argument must be supplied to 'data'.", call. = FALSE)
- }
- }
- else if (inherits(addlvariables, "formula")) {
- vars.in.formula <- all.vars(addlvariables)
- if (!is.null(data) && is.data.frame(data)) {
- data <- data.frame(data[names(data) %in% vars.in.formula],
- object$X[names(object$X) %in% setdiff(vars.in.formula, names(data))])
- }
- else data <- object$X
-
- addlvariables <- get.covs.matrix(addlvariables, data = data)
- }
- else if (!is.matrix(addlvariables) && !is.data.frame(addlvariables)) {
- stop("The argument to 'addlvariables' must be in one of the accepted forms. See ?summary.matchit for details.", call. = FALSE)
- }
-
- if (is.data.frame(addlvariables)) {
- addlvariables <- get.covs.matrix(data = addlvariables)
- }
-
- addl_assign <- get_assign(addlvariables)
- X <- cbind(X, addlvariables[, setdiff(colnames(addlvariables), colnames(X)), drop = FALSE])
- }
+ X <- .process_X(object, addlvariables, data)
treat <- object$treat
weights <- object$weights
@@ -273,6 +230,12 @@ summary.matchit <- function(object,
matched <- !is.null(object$info$method)
un <- un || !matched
+ chk::chk_flag(interactions)
+ chk::chk_flag(standardize)
+ chk::chk_flag(pair.dist)
+ chk::chk_flag(un)
+ chk::chk_flag(improvement)
+
if (standardize) {
s.d.denom <- switch(object$estimand,
"ATT" = "treated",
@@ -338,7 +301,7 @@ summary.matchit <- function(object,
int.names[k] <- paste0(nam[i], "\u00B2")
}
else {
- int.names[k] <- paste(nam[i], nam[j], sep=" * ")
+ int.names[k] <- paste(nam[i], nam[j], sep = " * ")
}
}
k <- k + 1
@@ -409,8 +372,10 @@ summary.matchit <- function(object,
## output
res <- list(call = object$call, nn = nn, sum.all = if (un) sum.all,
sum.matched = if (matched) sum.matched, reduction = reduction)
+
class(res) <- "summary.matchit"
- return(res)
+
+ res
}
#' @exportS3Method summary matchit.subclass
@@ -427,45 +392,7 @@ summary.matchit.subclass <- function(object,
...) {
#Create covariate matrix
- X <- get.covs.matrix(data = object$X)
-
- if (!is.null(addlvariables)) {
- if (is.character(addlvariables)) {
- if (!is.null(data) && is.data.frame(data)) {
- if (all(addlvariables %in% names(data))) {
- addlvariables <- data[addlvariables]
- }
- else {
- stop("All variables in 'addlvariables' must be in 'data'.", call. = FALSE)
- }
- }
- else {
- stop("If 'addlvariables' is specified as a string, a data frame argument must be supplied to 'data'.", call. = FALSE)
- }
- }
- else if (inherits(addlvariables, "formula")) {
- vars.in.formula <- all.vars(addlvariables)
- if (!is.null(data) && is.data.frame(data)) data <- data.frame(data[names(data) %in% vars.in.formula],
- object$X[names(data) %in% setdiff(vars.in.formula, names(data))])
- else data <- object$X
-
- addlvariables <- get.covs.matrix(addlvariables, data = data)
- }
- else if (!is.matrix(addlvariables) && !is.data.frame(addlvariables)) {
- stop("The argument to 'addlvariables' must be in one of the accepted forms. See ?summary.matchit for details.", call. = FALSE)
- }
-
- if (is.data.frame(addlvariables)) {
- if (!all(vapply(addlvariables, is.numeric, logical(1L)))) {
- addlvariables <- get.covs.matrix(data = addlvariables)
- }
- else {
- addlvariables <- as.matrix(addlvariables)
- }
- }
- }
-
- X <- cbind(X, addlvariables[, setdiff(colnames(addlvariables), colnames(X)), drop = FALSE])
+ X <- .process_X(object, addlvariables, data)
which.subclass <- subclass
treat <- object$treat
@@ -478,6 +405,12 @@ summary.matchit.subclass <- function(object,
kk <- ncol(X)
subclasses <- levels(subclass)
+ chk::chk_flag(interactions)
+ chk::chk_flag(standardize)
+ chk::chk_flag(pair.dist)
+ chk::chk_flag(un)
+ chk::chk_flag(improvement)
+
if (standardize) {
s.d.denom <- switch(object$estimand,
"ATT" = "treated",
@@ -489,8 +422,7 @@ summary.matchit.subclass <- function(object,
if (isTRUE(which.subclass)) which.subclass <- subclasses
else if (isFALSE(which.subclass)) which.subclass <- NULL
else if (!is.atomic(which.subclass) || !all(which.subclass %in% seq_along(subclasses))) {
- stop("'subclass' should be TRUE, FALSE, or a vector of subclass indices for which subclass balance is to be displayed.",
- call. = FALSE)
+ .err("`subclass` should be `TRUE`, `FALSE`, or a vector of subclass indices for which subclass balance is to be displayed")
}
else which.subclass <- subclasses[which.subclass]
@@ -645,7 +577,7 @@ summary.matchit.subclass <- function(object,
rownames(sum.sub)[1] <- "distance"
}
- return(sum.sub)
+ sum.sub
})
names(sum.subclass) <- paste("Subclass", which.subclass)
}
@@ -654,26 +586,14 @@ summary.matchit.subclass <- function(object,
qn <- qn(treat, subclass, object$discarded)
nn <- nn(treat, weights, object$discarded, s.weights)
- # if (subs) {
- # small.subclass.control <- which.subclass[qn["Control", as.character(which.subclass)] <= 1]
- # if (length(small.subclass.control) > 0) {
- # if (length(small.subclass.control) == 1) warning(paste0("Not enough control units in subclass ", small.subclass.control, "."), call.= FALSE)
- # else warning(paste0("Not enough control units in subclasses ", word_list(small.subclass.control), "."), call. = FALSE)
- # }
- #
- # small.subclass.treated <- which.subclass[qn["Treated", as.character(which.subclass)] <= 1]
- # if (length(small.subclass.treated) > 0) {
- # if (length(small.subclass.treated) == 1) warning(paste0("Not enough treated units in subclass ", small.subclass.treated, "."), call.= FALSE)
- # else warning(paste0("Not enough treated units in subclasses ", word_list(small.subclass.treated), "."), call. = FALSE)
- # }
- # }
-
## output
res <- list(call = object$call, sum.all = sum.all, sum.across = sum.matched,
sum.subclass = sum.subclass, reduction = reduction,
qn = qn, nn = nn)
+
class(res) <- c("summary.matchit.subclass", "summary.matchit")
- return(res)
+
+ res
}
#' @exportS3Method print summary.matchit
@@ -772,3 +692,102 @@ print.summary.matchit.subclass <- function(x, digits = max(3, getOption("digits"
}
cat("\n")
}
+
+.process_X <- function(object, addlvariables = NULL, data = NULL) {
+
+ X <- {
+ if (length(object$X) == 0) matrix(nrow = length(object$treat), ncol = 0)
+ else get.covs.matrix(data = object$X)
+ }
+
+ if (!is.null(addlvariables)) {
+
+ #Attempt to extrct data from matchit object; same as match.data()
+ data.fram.matchit <- FALSE
+ if (is.null(data)) {
+ env <- environment(object$formula)
+ data <- try(eval(object$call$data, envir = env), silent = TRUE)
+ if (length(data) == 0 || inherits(data, "try-error") || length(dim(data)) != 2 || nrow(data) != length(object[["treat"]])) {
+ env <- parent.frame()
+ data <- try(eval(object$call$data, envir = env), silent = TRUE)
+ if (length(data) == 0 || inherits(data, "try-error") || length(dim(data)) != 2 || nrow(data) != length(object[["treat"]])) {
+ data <- object[["model"]][["data"]]
+ if (length(data) == 0 || nrow(data) != length(object[["treat"]])) {
+ data <- NULL
+ }
+ else data.fram.matchit <- TRUE
+ }
+ else data.fram.matchit <- TRUE
+ }
+ else data.fram.matchit <- TRUE
+ }
+
+ if (is.character(addlvariables)) {
+ if (!is.null(data) && is.data.frame(data)) {
+ if (all(addlvariables %in% names(data))) {
+ addlvariables <- data[addlvariables]
+ }
+ else {
+ .err("All variables in `addlvariables` must be in `data`")
+ }
+ }
+ else {
+ .err("If `addlvariables` is specified as a string, a data frame argument must be supplied to `data`")
+ }
+ }
+ else if (inherits(addlvariables, "formula")) {
+ vars.in.formula <- all.vars(addlvariables)
+ if (!is.null(data) && is.data.frame(data)) {
+ data <- data.frame(data[names(data) %in% vars.in.formula],
+ object$X[names(object$X) %in% setdiff(vars.in.formula, names(data))])
+ }
+ else data <- object$X
+
+ # addlvariables <- get.covs.matrix(addlvariables, data = data)
+ }
+ else if (!is.matrix(addlvariables) && !is.data.frame(addlvariables)) {
+ .err("The argument to `addlvariables` must be in one of the accepted forms. See `?summary.matchit` for details")
+ }
+
+
+ if (af <- inherits(addlvariables, "formula")) {
+ addvariables_f <- addlvariables
+ addlvariables <- model.frame(addvariables_f, data = data, na.action = "na.pass")
+ }
+
+ if (nrow(addlvariables) != length(object$treat)) {
+ if (is.null(data) || data.fram.matchit) {
+ .err("Variables specified in `addlvariables` must have the same number of units as are present in the original call to `matchit()`")
+ }
+ else {
+ .err("`data` must have the same number of units as are present in the original call to `matchit()`")
+ }
+ }
+
+ k <- ncol(addlvariables)
+ for (i in seq_len(k)) {
+ if (anyNA(addlvariables[[i]]) || (is.numeric(addlvariables[[i]]) && any(!is.finite(addlvariables[[i]])))) {
+ covariates.with.missingness <- names(addlvariables)[i:k][vapply(i:k, function(j) anyNA(addlvariables[[j]]) ||
+ (is.numeric(addlvariables[[j]]) &&
+ any(!is.finite(addlvariables[[j]]))),
+ logical(1L))]
+ .err(paste0("Missing and non-finite values are not allowed in `addlvariables`. Variables with missingness or non-finite values:\n\t",
+ paste(covariates.with.missingness, collapse = ", ")), tidy = FALSE)
+ }
+ if (is.character(addlvariables[[i]])) addlvariables[[i]] <- factor(addlvariables[[i]])
+ }
+
+ if (af) {
+ addlvariables <- get.covs.matrix(addvariables_f, data = data)
+ }
+ else {
+ addlvariables <- get.covs.matrix(data = addlvariables)
+ }
+
+
+ # addl_assign <- get_assign(addlvariables)
+ X <- cbind(X, addlvariables[, setdiff(colnames(addlvariables), colnames(X)), drop = FALSE])
+ }
+
+ X
+}
\ No newline at end of file
diff --git a/R/weights.matrix.R b/R/weights.matrix.R
deleted file mode 100644
index d0fe224..0000000
--- a/R/weights.matrix.R
+++ /dev/null
@@ -1,15 +0,0 @@
-weights.matrix <- function(match.matrix, treat) {
-
- if (!is.integer(match.matrix)) match.matrix <- charmm2nummm(match.matrix, treat)
-
- weights <- weights_matrixC(match.matrix, treat)
-
- if (sum(weights)==0)
- stop("No units were matched.", call. = FALSE)
- else if (sum(weights[treat == 1])==0)
- stop("No treated units were matched.", call. = FALSE)
- else if (sum(weights[treat == 0])==0)
- stop("No control units were matched.", call. = FALSE)
-
- return(setNames(weights, names(treat)))
-}
\ No newline at end of file
diff --git a/build/MatchIt.pdf b/build/MatchIt.pdf
index 02f4bd1..3ddcc62 100644
Binary files a/build/MatchIt.pdf and b/build/MatchIt.pdf differ
diff --git a/build/stage23.rdb b/build/stage23.rdb
new file mode 100644
index 0000000..0e495c8
Binary files /dev/null and b/build/stage23.rdb differ
diff --git a/build/vignette.rds b/build/vignette.rds
index ad6f635..80b1196 100644
Binary files a/build/vignette.rds and b/build/vignette.rds differ
diff --git a/debian/changelog b/debian/changelog
index a74eff3..0d3ad23 100644
--- a/debian/changelog
+++ b/debian/changelog
@@ -1,3 +1,10 @@
+r-cran-matchit (4.5.4-1) UNRELEASED; urgency=low
+
+ * New upstream release.
+ * New upstream release.
+
+ -- Debian Janitor <janitor@jelmer.uk> Wed, 14 Jun 2023 16:14:10 -0000
+
r-cran-matchit (4.5.1-1) unstable; urgency=medium
* Team upload.
diff --git a/inst/doc/MatchIt.Rmd b/inst/doc/MatchIt.Rmd
index fa17963..eff196b 100644
--- a/inst/doc/MatchIt.Rmd
+++ b/inst/doc/MatchIt.Rmd
@@ -97,7 +97,7 @@ Below we assess balance on the unmatched data using `summary()`:
summary(m.out0)
```
-We can see severe imbalances as measured by the standardized mean differences (`Std. Mean Diff.`), variance ratios (`Var. Ratio`), and empirical cumulative density function (eCDF) statistics. Values of standardized mean differences and eCDF statistics close to zero and values of variance ratios close to one indicate good balance, and here many of them are far from their ideal values.
+We can see severe imbalances as measured by the standardized mean differences (`Std. Mean Diff.`), variance ratios (`Var. Ratio`), and empirical cumulative distribution function (eCDF) statistics. Values of standardized mean differences and eCDF statistics close to zero and values of variance ratios close to one indicate good balance, and here many of them are far from their ideal values.
## Matching
@@ -240,7 +240,7 @@ To report matching results in a manuscript or research report, a few key pieces
> We used propensity score matching to estimate the average marginal effect of the treatment on 1978 earnings on those who received it accounting for confounding by the included covariates. We first attempted 1:1 nearest neighbor propensity score matching without replacement with a propensity score estimated using logistic regression of the treatment on the covariates. This matching specification yielded poor balance, so we instead tried full matching on the propensity score, which yielded adequate balance, as indicated in Table 1 and Figure 1. The propensity score was estimated using a probit regression of the treatment on the covariates, which yielded better balance than did a logistic regression. After matching, all standardized mean differences for the covariates were below 0.1 and all standardized mean differences for squares and two-way interactions between covariates were below .15, indicating adequate balance. Full matching uses all treated and all control units, so no units were discarded by the matching.
>
-> To estimate the treatment effect and its standard error, we fit a linear regression model with 1978 earnings as the outcome and the treatment, covariates, and their interaction as predictors and included the full matching weights in the estimation. The `lm()` function was used to fit the outcome, and the `comparisons()` function in the `marginaleffects` package was used to perform g-comptuation in the matched sample to estimate the ATT. A cluster-robust variance was used to estimate its standard error with matching stratum membership as the clustering variable.
+> To estimate the treatment effect and its standard error, we fit a linear regression model with 1978 earnings as the outcome and the treatment, covariates, and their interaction as predictors and included the full matching weights in the estimation. The `lm()` function was used to fit the outcome, and the `comparisons()` function in the `marginaleffects` package was used to perform g-computation in the matched sample to estimate the ATT. A cluster-robust variance was used to estimate its standard error with matching stratum membership as the clustering variable.
>
> The estimated effect was \$`r round(est$estimate)` (SE = `r round(est$std.error, 1)`, p = `r round(est$p.value, 3)`), indicating that the average effect of the treatment for those who received it is to increase earnings.
diff --git a/inst/doc/MatchIt.html b/inst/doc/MatchIt.html
index 6486d7e..8a5bba1 100644
--- a/inst/doc/MatchIt.html
+++ b/inst/doc/MatchIt.html
@@ -12,7 +12,7 @@
<meta name="author" content="Noah Greifer" />
-<meta name="date" content="2023-02-22" />
+<meta name="date" content="2023-06-13" />
<title>MatchIt: Getting Started</title>
@@ -362,7 +362,7 @@ code > span.er { color: #a61717; background-color: #e3d2d2; }
<h1 class="title toc-ignore">MatchIt: Getting Started</h1>
<h4 class="author">Noah Greifer</h4>
-<h4 class="date">2023-02-22</h4>
+<h4 class="date">2023-06-13</h4>
<div id="TOC">
@@ -397,19 +397,19 @@ white-space: pre;
</style>
<div id="introduction" class="section level2">
<h2>Introduction</h2>
-<p><code>MatchIt</code> implements the suggestions of <span class="citation">Ho et al. (<a href="#ref-ho2007" role="doc-biblioref">2007</a>)</span> for improving parametric
-statistical models for estimating treatment effects in observational
-studies and reducing model dependence by preprocessing data with
-semi-parametric and non-parametric matching methods. After appropriately
-preprocessing with <code>MatchIt</code>, researchers can use whatever
-parametric model they would have used without <code>MatchIt</code> and
-produce inferences that are more robust and less sensitive to modeling
-assumptions. <code>MatchIt</code> reduces the dependence of causal
-inferences on commonly made, but hard-to-justify, statistical modeling
-assumptions using a large range of sophisticated matching methods. The
-package includes several popular approaches to matching and provides
-access to methods implemented in other packages through its single,
-unified, and easy-to-use interface.</p>
+<p><code>MatchIt</code> implements the suggestions of <span class="citation">Ho et al. (<a href="#ref-ho2007">2007</a>)</span> for
+improving parametric statistical models for estimating treatment effects
+in observational studies and reducing model dependence by preprocessing
+data with semi-parametric and non-parametric matching methods. After
+appropriately preprocessing with <code>MatchIt</code>, researchers can
+use whatever parametric model they would have used without
+<code>MatchIt</code> and produce inferences that are more robust and
+less sensitive to modeling assumptions. <code>MatchIt</code> reduces the
+dependence of causal inferences on commonly made, but hard-to-justify,
+statistical modeling assumptions using a large range of sophisticated
+matching methods. The package includes several popular approaches to
+matching and provides access to methods implemented in other packages
+through its single, unified, and easy-to-use interface.</p>
<p>Matching is used in the context of estimating the causal effect of a
binary treatment or exposure on an outcome while controlling for
measured pre-treatment variables, typically confounding variables or
@@ -426,8 +426,8 @@ balanced samples, a simple difference in means can be a valid treatment
effect estimate. Here we do not aim to provide a full introduction to
matching or causal inference theory, but simply to explain how to use
<code>MatchIt</code> to perform nonparametric preprocessing. For
-excellent and accessible introductions to matching, see <span class="citation">Stuart (<a href="#ref-stuart2010" role="doc-biblioref">2010</a>)</span> and <span class="citation">Austin
-(<a href="#ref-austin2011b" role="doc-biblioref">2011</a>)</span>.</p>
+excellent and accessible introductions to matching, see <span class="citation">Stuart (<a href="#ref-stuart2010">2010</a>)</span> and
+<span class="citation">Austin (<a href="#ref-austin2011b">2011</a>)</span>.</p>
<p>A matching analysis involves four primary steps: 1) planning, 2)
matching, 3) assessing the quality of matches, and 4) estimating the
treatment effect and its uncertainty. Here we briefly discuss these
@@ -437,10 +437,10 @@ other included vignettes, these steps are discussed in more detail.</p>
Supported Work program to demonstrate <code>MatchIt</code>’s
capabilities. First, we load <code>MatchIt</code> and bring in the
<code>lalonde</code> dataset.</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"MatchIt"</span>)</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(<span class="st">"lalonde"</span>)</span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(lalonde)</span></code></pre></div>
+<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"MatchIt"</span>)</span>
+<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a><span class="fu">data</span>(<span class="st">"lalonde"</span>)</span>
+<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a></span>
+<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a><span class="fu">head</span>(lalonde)</span></code></pre></div>
<pre><code>## treat age educ race married nodegree re74 re75 re78
## NSW1 1 37 11 black 1 1 0 0 9930.0460
## NSW2 1 22 9 hispan 0 1 0 0 3595.8940
@@ -521,8 +521,8 @@ taken in ensuring the effect generalizes to the target population of
interest. Different matching methods allow for different target
populations, so it is important to choose a matching method that allows
one to estimate the desired effect. See <span class="citation">Greifer
-and Stuart (<a href="#ref-greiferChoosingEstimandWhen2021" role="doc-biblioref">2021</a>)</span> for guidance on making this
-choice.</p>
+and Stuart (<a href="#ref-greiferChoosingEstimandWhen2021">2021</a>)</span> for
+guidance on making this choice.</p>
<p><strong>Selecting covariates to balance.</strong> Selecting
covariates carefully is critical for ensuring the resulting treatment
effect estimate is free of confounding and can be validly interpreted as
@@ -530,19 +530,19 @@ a causal effect. To estimate total causal effects, all covariates must
be measured prior to treatment (or otherwise not be affected by the
treatment). Covariates should be those that cause variation in the
outcome and selection into treatment group; these are known as
-confounding variables. See <span class="citation">VanderWeele (<a href="#ref-vanderweele2019" role="doc-biblioref">2019</a>)</span> for a
-guide on covariate selection. Ideally these covariates are measured
-without error and are free of missingness.</p>
+confounding variables. See <span class="citation">VanderWeele (<a href="#ref-vanderweele2019">2019</a>)</span> for a guide on covariate
+selection. Ideally these covariates are measured without error and are
+free of missingness.</p>
</div>
<div id="check-initial-imbalance" class="section level2">
<h2>Check Initial Imbalance</h2>
<p>After planning and prior to matching, it can be a good idea to view
the initial imbalance in one’s data that matching is attempting to
eliminate. We can do this using the code below:</p>
-<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co"># No matching; constructing a pre-match matchit object</span></span>
-<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>m.out0 <span class="ot"><-</span> <span class="fu">matchit</span>(treat <span class="sc">~</span> age <span class="sc">+</span> educ <span class="sc">+</span> race <span class="sc">+</span> married <span class="sc">+</span> </span>
-<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a> nodegree <span class="sc">+</span> re74 <span class="sc">+</span> re75, <span class="at">data =</span> lalonde,</span>
-<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a> <span class="at">method =</span> <span class="cn">NULL</span>, <span class="at">distance =</span> <span class="st">"glm"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="co"># No matching; constructing a pre-match matchit object</span></span>
+<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a>m.out0 <span class="ot"><-</span> <span class="fu">matchit</span>(treat <span class="sc">~</span> age <span class="sc">+</span> educ <span class="sc">+</span> race <span class="sc">+</span> married <span class="sc">+</span> </span>
+<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a> nodegree <span class="sc">+</span> re74 <span class="sc">+</span> re75, <span class="at">data =</span> lalonde,</span>
+<span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a> <span class="at">method =</span> <span class="cn">NULL</span>, <span class="at">distance =</span> <span class="st">"glm"</span>)</span></code></pre></div>
<p>The first argument is a <code>formula</code> relating the treatment
to the covariates used in estimating the propensity score and for which
balance is to be assessed. The <code>data</code> argument specifies the
@@ -558,8 +558,8 @@ default<a href="#fn2" class="footnote-ref" id="fnref2"><sup>2</sup></a>
(see <code>?distance</code> for other options).</p>
<p>Below we assess balance on the unmatched data using
<code>summary()</code>:</p>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Checking balance prior to matching</span></span>
-<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.out0)</span></code></pre></div>
+<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="co"># Checking balance prior to matching</span></span>
+<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a><span class="fu">summary</span>(m.out0)</span></code></pre></div>
<pre><code>##
## Call:
## matchit(formula = treat ~ age + educ + race + married + nodegree +
@@ -586,9 +586,9 @@ default<a href="#fn2" class="footnote-ref" id="fnref2"><sup>2</sup></a>
## Discarded 0 0</code></pre>
<p>We can see severe imbalances as measured by the standardized mean
differences (<code>Std. Mean Diff.</code>), variance ratios
-(<code>Var. Ratio</code>), and empirical cumulative density function
-(eCDF) statistics. Values of standardized mean differences and eCDF
-statistics close to zero and values of variance ratios close to one
+(<code>Var. Ratio</code>), and empirical cumulative distribution
+function (eCDF) statistics. Values of standardized mean differences and
+eCDF statistics close to zero and values of variance ratios close to one
indicate good balance, and here many of them are far from their ideal
values.</p>
</div>
@@ -602,14 +602,14 @@ score, which is appropriate for estimating the ATT. One by one, each
treated unit is paired with an available control unit that has the
closest propensity score to it. Any remaining control units are left
unmatched and excluded from further analysis. Due to the theoretical
-balancing properties of the propensity score described by <span class="citation">Rosenbaum and Rubin (<a href="#ref-rosenbaum1983" role="doc-biblioref">1983</a>)</span>, propensity score matching can be
-an effective way to achieve covariate balance in the treatment groups.
-Below we demonstrate the use of <code>matchit()</code> to perform
-nearest neighbor propensity score matching.</p>
-<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co"># 1:1 NN PS matching w/o replacement</span></span>
-<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>m.out1 <span class="ot"><-</span> <span class="fu">matchit</span>(treat <span class="sc">~</span> age <span class="sc">+</span> educ <span class="sc">+</span> race <span class="sc">+</span> married <span class="sc">+</span> </span>
-<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a> nodegree <span class="sc">+</span> re74 <span class="sc">+</span> re75, <span class="at">data =</span> lalonde,</span>
-<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a> <span class="at">method =</span> <span class="st">"nearest"</span>, <span class="at">distance =</span> <span class="st">"glm"</span>)</span></code></pre></div>
+balancing properties of the propensity score described by <span class="citation">Rosenbaum and Rubin (<a href="#ref-rosenbaum1983">1983</a>)</span>, propensity score matching
+can be an effective way to achieve covariate balance in the treatment
+groups. Below we demonstrate the use of <code>matchit()</code> to
+perform nearest neighbor propensity score matching.</p>
+<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="co"># 1:1 NN PS matching w/o replacement</span></span>
+<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a>m.out1 <span class="ot"><-</span> <span class="fu">matchit</span>(treat <span class="sc">~</span> age <span class="sc">+</span> educ <span class="sc">+</span> race <span class="sc">+</span> married <span class="sc">+</span> </span>
+<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a> nodegree <span class="sc">+</span> re74 <span class="sc">+</span> re75, <span class="at">data =</span> lalonde,</span>
+<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a> <span class="at">method =</span> <span class="st">"nearest"</span>, <span class="at">distance =</span> <span class="st">"glm"</span>)</span></code></pre></div>
<p>We use the same syntax as before, but this time specify
<code>method = "nearest"</code> to implement nearest neighbor matching,
again using a logistic regression propensity score. Many other arguments
@@ -618,7 +618,7 @@ score estimation.</p>
<p>The matching outputs are contained in the <code>m.out1</code> object.
Printing this object gives a description of the type of matching
performed:</p>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>m.out1</span></code></pre></div>
+<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a>m.out1</span></code></pre></div>
<pre><code>## A matchit object
## - method: 1:1 nearest neighbor matching without replacement
## - distance: Propensity score
@@ -663,8 +663,8 @@ before. Here we set <code>un = FALSE</code> to suppress display of the
balance before matching for brevity and because we already saw it.
(Leaving it as <code>TRUE</code>, its default, would display balance
both before and after matching.)</p>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Checking balance after NN matching</span></span>
-<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.out1, <span class="at">un =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="co"># Checking balance after NN matching</span></span>
+<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="fu">summary</span>(m.out1, <span class="at">un =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
<pre><code>##
## Call:
## matchit(formula = treat ~ age + educ + race + married + nodegree +
@@ -696,22 +696,20 @@ is not sufficient for removing confounding in this dataset. The final
column, <code>Std. Pair Diff</code>, displays the average absolute
within-pair difference of each covariate. When these values are small,
better balance is typically achieved and estimated effects are more
-robust to misspecification of the outcome model <span class="citation">(<a href="#ref-king2019" role="doc-biblioref">King and
-Nielsen 2019</a>; <a href="#ref-rubin1973a" role="doc-biblioref">Rubin
-1973</a>)</span>.</p>
+robust to misspecification of the outcome model <span class="citation">(<a href="#ref-king2019">King and Nielsen 2019</a>; <a href="#ref-rubin1973a">Rubin 1973</a>)</span>.</p>
<p>Next is a table of the sample sizes before and after matching. The
matching procedure left 244 control units unmatched. Ideally, unmatched
units would be those far from the treated units and would require
greater extrapolation were they to have been retained. We can visualize
the distribution of propensity scores of those who were matched using
<code>plot()</code> with <code>type = "jitter"</code>:</p>
-<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.out1, <span class="at">type =</span> <span class="st">"jitter"</span>, <span class="at">interactive =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
-<p><img 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" alt="Jitter plot of the propensity scores, which shows that no treated unit were dropped, and a large number of control units with low propensity scores were dropped." /></p>
+<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a><span class="fu">plot</span>(m.out1, <span class="at">type =</span> <span class="st">"jitter"</span>, <span class="at">interactive =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<p><img 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Uod45ax5557JljlYiF4yimnhMvL1aFfVSHNn2H7N998M3n00UcTrLPGmTJaMAHKuQUXXDDBgv7ZZ58l3u8xjc8zSohFG1ZQrMfUl19lIcRFWFmohj35WdniiW5XXXVVEHs8l7haEPKei1LPEc+4pU0nzgKWX45jIcxaBy2OffK8mCXT0rJPjl900UUWNXy+/PLLqRBEPGOJxVUhrhu+R4T4WLn6KlX3iGuzXtOZoB4RufH9W/tSDe9QSP0TgSYhIAHaJBWhYjQ/gUoE6MCBA9MX5GWXXRZuKu8lu/fee6fxeNEfdthhQVDGVp2YiL0gYx/Q+AVmFrX4mnICFLEbh/jlxpA+oRoBSvxivnt5DC6++OKUgQky0iDw8rZ7tiHvWAT5CT7/jfi//1iQiY8luFSoNs9bbrklLceNN95YKulwLhagVv7s56qrrpr6+parwz59+oT8cTWInw2EoqU7//zzp+WKBShuChYQLwzbcw1DxwT8lNnHyhb7NOICYmn7JYpC3GrY429qljusqtTlpZdemnz++echrfhf3nPB+WLPEcPdJuQR+ATEoFl77Vg4UeIfZfSTjJJ55pknvVe7Zz6POeaY9Go/8SyN88ILL6TH/QS/BL9eBOmff/4ZjldTX6Xqno6GlQf3CwvU4wQTTBDOmatBNbwtHX2KQDMQ0EL0/luuIAL1IvDJJ5+kSXk/v3Q7u+FflM4PH4fD3t8szLplNi5rRfoXjmPN0WqCf5FWEz3E9X6CBdfMNNNM6b63UKXbbPjGqmDfW20L9juz433t0su8KEu32Yj3WasxG1izMQ7MaCb4yUjx4Q7bteTZIbEyB6affnrnh4yd72CEmdasIctM65tuuimsyZq9PFuHflKQ8xbXEI21Q72oSy/xvsPOD2uHfdaGzdYP6456K2ka31uMnbeohX0vXsMnM78JftjdecteOE8c3xEJx/nnO13ptm2UY8+amjzDhJ9//tn5jpjbdNNNnbcuuvnmm895y50lVfWnF1/Oi+xwHRy9KHfe4u+8n2w45sVuRWlSRt8JdN7FxXn/Seets84P/afX+g5h+iyxEoIF7/phm84vz+V8hzDUgxfFYQZ9Z+srW/dWN2TmOz5p3fjOg/MW+FAGq5tG8k5vVhsi0AACEqANgKokuy8Be7lDgKWaigXOsbA5L8H4hc6ySX743S299NLh5Vrs+uxxFsKvNvjh74JLECIWeKHGgRd9HMoJvThusW0/bJ6eisUVB71vXnoubyNb9uz1eddwrJY8i6VZ7DjCgSV9qGc/bO78MK1ba621csUnaWTrMC4rAjIbjJF9Zs/H18fnrJ5NyMCOBfbtj04QQpE/7zMZXxq2K2HPgvyIbZ5j7y8ZrkMkI/joXNxwww0d0q30gP2Qg18NwA0ZMiRNiw6fLVdWLC3v5uH85Lyw/JItr+Wt5uGHIrxLh6NjSPDD+O7pp58O23Fnq9RzH/Outr6ydW91QwFYqsrqhk86CdTNpJNOGsrHv0byTjPRhgjUmcD/v3HqnLCSE4HuRsAv4+L87PBw2/POO2+wZpZi4H0t3XbbbRcsJ1gczz33XIfVjOD9zpwfhutwedbSZRGweFUbsLzGwU+aSnexsBHspeqHGB1/Fvxwqm0W/SxWVruAXxSy4H1XbTN8xlYys/TFEYqJrjhO3nYteealV89j2Tr0vn9p58QPp7tYCLHerB/6DdnPPPPMHQQ7dWXnicS1rE1LMMv8lFNOGfbpXPDrTU899VT48/7AQdixz3ql2VAJe4Qr9eZdGJxfZsw98MADbvvtt0+TQoxXGrLPkV8xwcGG4If201+e4lfGygW+U+eff34oV2zptetYH9SC1Uds9Yy/IwhgP0EsWHtZ47WW+rK8LG+rG/b5tTKrGz75rvBJB8dCPXlbmvoUgUYTkABtNGGl35YE/ESJ8BK7+eab3SWXXOL8LFrHkDYvSyxMfsmWkvc911xzOYYT+UTM8cLBMhP/TCNDfBbMIuknHATrjC0wb+crtQBafD79bOEwHMw2AoHFvwlYYxDQBBPEbJtI9P6Crtgi3VZO4uOOwPBmsYDLgVnjEANYzBiyPeuss9JyYSn2M3yLJVH18eGRZ6WFzKtDu3dcMnbcccfwQwcvvvhi+MlXS3fLLbe0zYJPfnIVKx8C0K8OEK4lgrlemJsDAvSEE04IQ84IVY5jaeOHDLx/YUGalewgjOi48KMJDGVjMcUdwK+7mf7EZdbil0231HOEddHEJkPnfvJUuHyjjTbKJtNhf7XVVkvFOlZDuPiljsKPBPDTtHwnCLjH2HcAwWuie/fddw/WbLgcfPDB4XvDiIWfsBau62x9ZeueoXbvTxrSRKxbZ9TPjA9CF37WxtSDd8hI/0Sgqwn4F6aCCIhABQTiSUj+e5pOEoi3vaAK61PGyeVNtGDtTP9SC2l460eyyCKLJF6Epmn6F2WcRIelY1jOKZ7EEM82tgvLTULq27dvyC9e3oV78ULUkgjLQVk5Occ6p/5lmbCOpd03s9ctMBvYjnMdPJigkceAa+BAenZNvM1SN6w3aiGeCMOkrTiwHBFp+BdzfDh3u5o8a5mElF2IPq8w5eqQ1Q78cGsuH+6XJYviYJOQ4OhFXFgGySboEJ/li/jhAwKL97MEkbFnSat4Oa+4XqthzyoBLLtk6fKc8Xx7URqO8bz74e1QhmLPRbHnKFzk/zE5ytLnk0lolYa99tqr4No4HbYpn00gtDSZ3BTHi59Tlj3jngnV1Fe5umfVgDgf/ytq6TJZbNuPBlTD2+5HnyLQDARkAfWtioII1EIAaxETeNZcc83g74fVp1zAWuOXwXH8NCfDpfgIMukEaxFWFiZuxMGvzZkOh3PcJl3EcardJg8sSeZvhksAViG/bFCaFJNTTj/9dOdFajjGJAsmgdjPSqYR/7fBObMC+QbOkaZfFD8bLd2HA/6AfsmgYB1jKNG/dMOkHT/jOE0rvaAOG8Mjz84WG0sXrPF75KddzScT6zg/Cxq7KsR5MDEFqzb+jfas4DfIT8ZaXTLs60VQmCBEfKys/HlBFSzcvgMTJ1nxNs8wljrKjP8n7gI83zxnWPb8OqypdbFYouWeI7433I+FSicfEd8v8+XwBY19rzmO1ZZ7h5tZWDlOOOecc8J1xo56wHrPJCTcF8wvtrP19d9cCv/7H4FwfgUIhwsAFlisoNQ7P/HKdwbfUEI9eBfmrD0R6BoCPVDBXZOVchEBEcgjwJA2Q+u8vBAZeRMYuI4XuLdEOW9RCr9jnZdWZ44hUBAJvOgQf3mBoVlmA5O3+YXmxbNjCFWENf6GvDwrCUz84P64ppI8KkmzXJzhkWe5MpU67y2GQfjY8Gw2rl8n1uEvyrNkk2xginCyVRey17DPa4AJdFyHa0i9AnxxxeAZo8ykX00o9RzhIsAkL0Qurh7Vpk05cE+g48cqCrjBmEtIqTLyXWHYn/gmAovFL1dfxa7LHsc1BY64xJQqY628s/lqXwQaSUACtJF0lbYIiIAIdCGBPAHahdk3PCv8pf3Qc7BIYq0n+F8fc1deeWXD81YGIiAC9SXQcW2P+qav1ERABERABESgLgSYKBTPoMf6eeCBB9YlbSUiAiLQtQQkQLuWt3ITAREQgYYRwI2C2dv4dLZjwPeTGfK4dbDIP36w8Q8otOM9655EoF0JaAi+XWtW9yUCIiACbUjApi1U6lvchgh0SyLQFgRkAW2LatRNiIAIiED3ICDh2T3qWXfZ/gS0DFP717HuUAREQAREQAREQASaioAEaFNVhwojAiIgAiIgAiIgAu1PQAK0/etYdygCIiACIiACIiACTUVAArSpqkOFEQEREAEREAEREIH2JyAB2v51rDsUAREQAREQAREQgaYiIAHaVNWhwoiACIiACIiACIhA+xOQAG3/OtYdioAIiIAIiIAIiEBTEZAAbarqUGFEQAREQAREQAREoP0JSIC2fx3rDkVABERABERABESgqQhIgDZVdagwIiACIiACIiACItD+BCRA27+OdYciIAIiIAIiIAIi0FQEJECbqjpUGBEQAREQAREQARFofwISoO1fx7pDERABERABERABEWgqAhKgTVUdKowIiIAIiIAIiIAItD8BCdD2r2PdoQiIgAiIgAiIgAg0FQEJ0KaqDhVGBERABERABERABNqfgARo+9ex7lAEREAEREAEREAEmoqABGhTVYcKIwIiIAIiIAIiIALtT0ACtP3rWHcoAiIgAiIgAiIgAk1FQAK0qapDhREBERABERABERCB9icgAdr+daw7FAEREAEREAEREIGmIiAB2lTVocKIgAiIgAiIgAiIQPsTkABt/zrWHYqACIiACIiACIhAUxGQAG2q6lBhREAEREAEREAERKD9CUiAtn8d6w5FQAREQAREQAREoKkI9Gqq0rRQYU4//XR3zjnnuLHHHruFSq2iioAIiIAIiIAItDuBUUYZxQ0ePNiNOeaYTXurPRIfmrZ0TVyweeaZxx155JESoE1cRyqaCIiACIiACHRHAosvvrh76qmn3Oyzz960ty8LaCerplevXm6sscZy888/fydT0GUiIAIiIAIiIAIiUH8C0003Xf0TrXOK8gGtM1AlJwIiIAIiIAIiIAIiUJqABGhpPjorAiIgAiIgAiIgAiJQZwISoHUGquREQAREQAREQAREQARKE5AALc1HZ0Wg2xNYcskl3UILLZTL4eGHH3Y9evRwDzzwQO75rj6I0/1jjz1WcbasYnHCCSdUHL+aiD179nTnnntu7iWbb7554Aa7Yn8nnXRS7rX1PPjNN9+4Sy65pOYkb7rppnAf3377bW5aHOc+Tz311NzzO+64oxtvvPFyzxU7uM0227j+/funp6ut+/RCbYiACAwXApqENFywK1MREIF6E/j777/dggsu6C6++GK38MIL1zv5uqa37bbbumWWWSZNc4899nB9+/Z1u+66a3pszjnnTLcbtUE5fv/9d7fZZps1KouGpUunaLLJJgvpt1LdNwyIEhaBFiMgAdpiFabiioAI5BNgRblWWVWO1TPiFTQOPfTQIEDXX3/9/Jtr0NF///03WCYblHxDk910003T9Fup7tNCa0MEujkBDcF38wdAty8C9SRwxBFHuAMPPNBdddVVDgveuOOO65Zffnn34YcfptkQ56CDDnKDBg1ys802m+vdu7fbeeedg3g866yz3AwzzOBmmWUWd8opp6TXsDFs2DC35pprun79+oXFlRl+JR/Cb7/9lgq6ww47zMXi5IILLnCLLbaYm3DCCd3GG2/scBuIwx9//OF22mmnUI6pp57a7bfffh2ELFbVueaaK+Q777zzhgWe4zS4vy222MJNMskkYVj4vvvui0/XtI0197bbbnMLLLCAm2aaadxdd90V0nvttdeCFZWh62mnndYdcMAB7q+//krz+vPPP93+++8fGI8++uiOZVl22223wIpIu+yyi3vooYeCy8Lcc8/tPv/884rSJdIdd9zhlltuOTf++OO7VVdd1X355Zfh2nr9q+Q5Ouqoo0J9Fqv7n376yeHqQJ3AiHURn3766XoVUemIgAjUSEACtEaAulwEROD/CSDE8ClExK2yyiru4IMPdk8++aRbe+2100jEueiii9yxxx7r8ONbeeWV3ZlnnumWXnppd8YZZ7jtttsuDKUzLP3SSy+F6xATCM4vvvjCHXLIIQ7/yJFGGsltuOGG7vXXXw/bO+ywQ4iLz6rlR1k4jog7//zzHelQrthXESHzzjvvOEQPIu+4445zp512Wlpe8qKcCLhLL700xEF03XzzzSEOQm+ttdZyzz77rOMX0hjO3mCDDRzWxXqEF198MdwDPqXTTz+9m3TSSd1bb73lEMLcx4knnuhgdd5554W8Lc9NNtnEXXjhhYER5V5kkUXCfR1//PEhCtynmmqqIOgR4PxiSiXpvvzyy+F+J5544uDugMDbfffdLdu6fFb6HL3xxhtF655ODb7JPIP8ah1hwIABBXVfl8IqEREQgc4R8EMXCp0g4IfPkieeeKITV+oSEWgtAksssUTifStzC+0taPySWnL//feH81tuuWXihVIydOjQNL4XSCHO999/n8bhGi8e0jhTTjlliPPuu++mx6aYYorEi8Kwf+eddybe8pl88MEH6XnyIB0vLMMxLwTDvreshn1vDUxGHXXUxIvh9Brv75iQrp94FI75H5NIvKU24VoL3gqaeEEZdn/44YfET1RKvEXVTodPL3ATb3UM2174Jf6HKZKPPvoojXPFFVeEsnjhkx4rteHFbbLOOuvkRhlttNGSOeaYI/nnn3/S85TPW/US7sfC9ddfH/L0gjX59ddfE29FTrxF2U6HT/+rKIkX+umx1VZbLVlhhRXS/XLpEnGppZZKvKBPr2HDC/KQt5/UVHDcdjhOXXmrth0q+PSdhMRby9NjlTxHW2+9deKt0uGabN1z0PvUJt4qnKb56aefJl6UJ95ynB7Thgi0KwE/upT4DnxT3558QH2rqCACItA5Ar5163Dh5JNPHoaE7YT9IgfWx3HGGSccnmiiidyMM85oUdyss87qRhxxRMcQuAUmmGCZJDCMzxA8gSHzt99+O/zM3AgjjJAOKYeT0T8vUMMEG7OGcorfR8a6xoxsC4suumjI2/axFDK8TcAC++OPPzp+evf555+3KG6mmWZyXvA5ZpEThyFsL2zT815MOiyQ9QqUiXu1gBsB5cb6awGmxPEdY+cFq3v11VfDKerIC/dQTqyyXpzaJR0+K0mX+2VoPw7rrbeeu/XWW+NDNW9X8hyVygSL+cknnxyeDyzWWMGxBCuIgAg0BwEJ0OaoB5VCBJqWgLciOm8JzC3fzz//HI7jC2hhggkmsM3wOfLII4dPb8FLj9vsZTvA8LK3cNpu+ORYHBhiZoieYWLOIQoRV3kimOvefPPNcDlD1nGIxSfH8UGNAyKVWdUEhBuBIeq8wHmELsPRcUBM43NarxCLW3wev/76a3fLLbeEv2weCGwCLgH42j7++ONBdNIR4Noxxhgje0nYryTd7777Lohuht3jkK3P+BzbPEMEOg95gc5J9rnJ7uc9R3lp2THcD7baaqvwzLD8E88oyz0NHDiwQMxbfH2KgAh0LYH/71J3bb7KTQREoEUI8OIuNsnEJq7gS2ghK/DsePyZFZfxubztyy+/PPiGYvW75557nB/OdzbRp5gAZY1PAhbMOHBtPFmnVHmZREV49NFHg4jDehj/MTEJS12eQC9laYzLU8m2H+JPoyHmEMn4XcZlse0jjzwy+MqyzBMdBITYZ599FqzGpZZ2qiRdLNhMaIJhHMrdq3cjCGUu9hzh2xs/Q6Rdql7ivIttU3c33nij++qrrxzPD6sOHH744cFPt9g1Oi4CItB1BCRAu461chKBliTA8LL3n3MPPvhgh/KzADmWPu9L2eFcPQ9g7Zt55pnd2Wef7ZhkhKBhiBnxaZZVEyw2+YcJOxzLznzmeiY6VRLMTYBZ6ORpf9ddd11IAyHLcDfD0mYNJl0mx/zyyy+VZFF1HO6JlQJgYuXhExHHUDiTlhhKRxQjPjmGFRguDNkbLzImLeNVSboM8Xs/0iDI44KzCHy5gMUajtkOAdZcfjwgdr8ol1b2PGUn2L1gaWUiGG4BzIDfaKONwsoFiFwswwoiIALDn4AE6PCvA5VABJqawGZ+VjeWM/waWRqJIV2WAuL4vffe2yUWJRYdZ6gboYPIQPCxpBKBoWMCVkKGaVlaiJnaDFuvu+66YVkoZsMzY5zZ7a+88koHH8aQQM4/ljfCh5RlmBC/ZnllCSMsn+TH2p3MIGemPHkMGTIkLVtOknU5xJJL+MQypPzee+8FtwQ/cSdsU1dY+xCLCGXEGJZqyvf++++nvCgIw/G4KmDhZUH6culyzZ577hksizBh6Jyls4r9whHxLbDqAeVYaaWVgkWSjsHVV1/t/KSmsFxX1q/UrqvkM1v3WIipG9JkFQY6A4jRD7zLBL6gCiIgAk1AwFsQFDpBQLPgOwFNl7QsAS8cEi8cEu+XF2YzM+vbLwPUYZY1s5e9xbTgPv2QebjGi59wPC/O6quvnvgh44LrvOgMs5Y56H0PE2Zs+2HixAuLxPsgJl5UhmtWXHHF9Dq/zmWYke6tsuEY13kLYJiZ75vbxFsKEy+W0vjMgvfLEqX7bHjraJh1bge96Azl4J5Jw1sTE2Zge+FrURI/QSnMyPaWuJA/s6+9b2lSr1nwfjJNmpdteEEcZo5TJrjA4bnnnrPTiR9uTrxvZuKXqwp/XqwmfkmpsA0XgndjSGBAGqxoQCiXLnG8kA/3x3XeRSM5+uijQxrFZsFzDWHw4MHJfPPNF+qQa1lhgFn43ir53wj/+5/3jGSfo3gWPJdl6977wiZrrLFG4t0GQtmo+3322adgNYGCTLUjAm1EoBVmwfeAt28IFKokwHqBzLDkU0EEuhMBJrkwQQRfwK4OWDuZCIP1sVjAkoeVNC4fx3Aj6NOnT1g3sti1pY6z3idp9POTpWzINxsfH0esoQyJd0Wg+f7444+DGwQ+nNnAeax+8GJiVF4gDpZdhqotlEuXeMTxS08FS3M8Q9/SKPWJ6wLX+uW36johKK/ucTkgL37qtNpylroHnROBZiaAq8xll10WXGaatZz/79nerCVUuURABJqKAC/y4RUQduXEXZ4Q4xi/IlRLYOF7BFOpkJ0NXypuPc4hhBHVxQLny5WZOLH4JK1y6Vqczj4L2SW3ipW/2uN5dc+Et3IMqs1H8UVABGonIB/Q2hkqBREQAREQAREQAREQgSoISIBWAUtRRUAEREAEREAEREAEaicgAVo7Q6UgAiIgAiIgAiIgAiJQBQEJ0CpgKaoIiIAIiIAIiIAIiEDtBCRAa2eoFERABERABERABERABKogIAFaBSxFFQEREAEREAEREAERqJ2ABGjtDJWCCIiACIiACIiACIhAFQQkQKuApagiIAIiIAIiIAIiIAK1E5AArZ2hUhABERABERABERABEaiCgARoFbAUVQREQAREQAREQAREoHYCEqC1M1QKIiACIiACIiACIiACVRCQAK0ClqKKgAiIgAiIgAiIgAjUTkACtHaGSkEEREAEREAEREAERKAKAhKgVcBSVBEQAREQAREQAREQgdoJSIDWzlApiIAIiIAIiIAIiIAIVEFAArQKWIoqAiIgAiIgAiIgAiJQOwEJ0NoZKgUREAEREAEREAEREIEqCEiAVgFLUUVABERABERABERABGonIAFaO0OlIAIiIAIiIAIiIAIiUAUBCdAqYCmqCIiACIiACIiACIhA7QQkQGtnqBREQAREQAREQAREQASqICABWgUsRRUBERABERABERABEaidgARo7QyVggiIgAiIgAiIgAiIQBUEJECrgKWoIiACIiACIiACIiACtROQAK2doVIQAREQAREQAREQARGogoAEaBWwFFUEREAEREAEREAERKB2AhKgtTNUCiIgAiIgAiIgAiIgAlUQkACtApaiioAIiIAIiIAIiIAI1E5AArR2hkpBBERABERABERABESgCgISoFXAUlQREAEREAEREAEREIHaCUiA1s5QKYiACIiACIiACIiACFRBQAK0CliKKgIiIAIiIAIiIAIiUDsBCdDaGSoFERABERABERABERCBKghIgFYBS1FFQAREQAREQAREQARqJyABWjtDpSACIiACIiACIiACIlAFAQnQKmApqgiIgAiIgAiIgAiIQO0EJEBrZ6gUREAEREAEREAEREAEqiAgAVoFLEUVAREQAREQAREQARGonYAEaO0MlYIIiIAIiIAIiIAIiEAVBCRAq4ClqCIgAiIgAiIgAiIgArUTkACtnaFSEAEREAEREAEREAERqIKABGgVsBRVBERABERABERABESgdgISoLUzVAoiIAIiIAIiIAIiIAJVEJAArQKWooqACIiACIiACIiACNROQAK0doZKQQREQAREQAREQAREoAoCEqBVwFJUERABERABERABERCB2glIgNbOUCmIgAiIgAiIgAiIgAhUQUACtApYiioCIiACIiACIiACIlA7AQnQ2hkqBREQAREQAREQAREQgSoISIBWAUtRRUAEREAEREAEREAEaicgAVo7Q6UgAiIgAiIgAiIgAiJQBQEJ0CpgKaoIiIAIiIAIiIAIiEDtBCRAa2eoFERABERABERABERABKogIAFaBSxFFQEREAEREAEREAERqJ2ABGjtDJWCCIiACIiACIiACIhAFQQkQKuApagiIAIiIAIiIAIiIAK1E5AArZ2hUhABEfAE5pxzTtejRw+32Wab5fL44osvXM+ePUOcjz/+ODdO3sF///3XnXPOOe6PP/7IO93h2D333BPy+OCDDzqcq8cB7uHcc8/NTWrzzTcPecOh2N9JJ52Ue209Dz711FPuscceqznJ9ddf3y255JJF0/n222/DfZ566qm5cXbccUc33njj5Z4rdnDcccd1xx13XHq6XveSJqgNERCBpiAgAdoU1aBCiEB7EECcDR482P39998dbujmm292iMlqw3XXXed22GEH988//1R7aZfH33bbbd1VV12V/k0yySRuvvnmS/c5t+KKKza0XLBfcMEF3bvvvtvQfBqV+IYbbuhmnXXWkHyr30ujGCldEWgHAr3a4SZ0DyIgAs1BYOGFF3aPPPKIe/jhhztYzm644QY399xzu+eee66qwnZGtFaVQR0jzz///I4/C4ceeqjr27evw5LYVSFJEsdfq4YzzzwzLXqr30t6I9oQARHoQEAW0A5IdEAERKCzBCabbLIgwG666aaCJL7++usgTNdaa62C4+zccsstDuHKUO2kk07qVlhhBffWW2+FeLfddps76KCDwvYiiyziLr/88rD9yy+/uF133dXNOOOMbtppp3UHHHCA+/TTT8M5+zd06FC39NJLu3HGGSeUCctsHH799ddgWZ1yyindhBNO6FZbbTX30UcfxVHchx9+6LbYYguHJbN///7uvvvuKzjf2R2Gx7FSkt7kk0/uFltsMffdd9+F5C6++GI311xzuTHHHNPNO++8waIc5/Pnn3+6/fff380222xu9NFHd9NNN53bbbfd3G+//Rb+TAAfdthhbtNNN00vfe2119wyyywTOBuzv/76Kz2P0D/88MOD9bFPnz5u4MCBnbJYpwnmbAwYMMDde++9buedd3ZTTDFF+Ntrr71cXI7FF1/cXXrppUXv5dlnn3WkAx/qDreH77//Pic3HRIBEWhmAhKgzVw7KpsItCCBNddcM4jK2AqHyJxjjjlcv379Cu7oxhtvdKuvvrqbZZZZ3AUXXOC22WYb98ILLzjSICAwEU0EhrfxM0UobbDBBg6Ru88++7j99tvPXX/99Q5/wzisscYaQaQde+yxDtG29tpru/feey9EoWxLLbVUuI4hX3xM8WecZ555UjHDNQhmBM/pp5/uNvO+reRbD4vsjz/+6J555hm39dZbu4UWWsiNNdZYQRjiHwoDRCUibIEFFnCrrrqqw33BwiabbOIuvPBCR7mJgzA/7bTT3PHHH+9GGmmkIKqJi+8m90xA0CNmuccTTzzR7bHHHu68884L9xQi+H/HHHOMgxWCjnLceeed7tZbb7XTdfl86aWX3JZbbhnqmE7D8ssvH/Ki/BaIg79w3r3QaaCDMtpoowUG3Mfdd98dWNj1+hQBEWgRAr4hVugEAW9lSJ544olOXKlLRKA9CXiBmXiBlrz//vuM/yZPPvlkeqNeRCZ+YklyzTXXhHPe0hjO7bLLLokXIWk8No466qgQx1u1wvErr7wy7HurZ9j3YizsP//882Gffw888EDirYGJF1qJFyThvLcApue9qA3Hrr322nCMT8p4++23p3G8uEm8sEkOPPDAcMwLtKRXr16JlZWDV1xxRbjOC9b0ulIbXkgm66yzToco5Ev+RxxxRHruhx9+SMYee+zEWy3TY2x4EZl4i2U4Rhm9WE/OOuusgjizzz574q294ZgXziHtQYMGpXG8kE68hTn5/fff02NetId4L774YuIt1In33028EEzPe4tsYOotkumx7MY333wT0jjllFOyp8K+991N/KSi9JwX2om3JCdexKfHvI9s4jsD6b63WCdeCIf97L08/fTTaZntAu6D5yhO087pUwS6KwE/QpL4zlxT3758QFuko6BiikCrEGBYFGsnVjuGgxlaHjJkiDv77LM7+H/Gli+sc6+//rp75513wq0ypMzweTa8/PLLbqqppgrD1HZuiSWWcD///HPB7Pd49jblGWOMMZzNjMdP1Yu9MPTuhawlEyysjz/+eNjHEofPKkPFFryYdFgg6xUYerdAflhGscLGZZppppmCpdaLPTfBBBO4V199NVzi3yzhfrgOqyzWwWIBn9xFF1008LU4E000kRthhBGc70gHSzOTvLBGW2A2+rLLLptahO14rZ8MsbNCgAXcAXCXqCTAAusnll0s4qusskqwUue5dlSSnuKIgAgMPwISoMOPvXIWgbYlwBD6ZZddFpbTYRiXIfapp566gwD1Vr/gv8kQ/eeffx6GofE/JCCw8sKbb74ZfEWz52JRw7nevXunUTjHkK7NzkeIIvaYoZ4N5iaAKJp44okLTo844ohBtBYcrGEnFrcmjnfaaafcFDmPAMUlAL9YhDKik+F6xDoCOy9wDh9cGPOXDfi5snoBAV/XOODTW8q/ctRRRw3Riy2R9dNPP4Uyx2nibxuHUUYZpWK3Bu6RZba22247t/fee4c/ni3cChDLCiIgAq1DQD6grVNXKqkItAwB/C+xZGLRxM+zmIVq4403dn5Y3u27774OyyZWPvz6SgUsl4jHbEBkxaI1K0jj+FhWEZpMZkLExX9vvPFGiMrkIARyNpSyNGbjlts34Uc8LI6ERx99tKA8VjYmJuEbiU8s1l78QD/77DP39ttvB8ttuDjnHyIRkbf77rvnpnvkkUeGiVBcmhWb8CkVsEaS9pdffpkbjfJirY5DqXqJ4xXbZsIaE6roiOD3iq8uS1shpBVEQARah4AEaOvUlUoqAi1DgKHS6aefPqx/6f0zcwUowoEJJMxm549Z3YiTV155JdynWStNsNjknxlmmCGsccmQvQXELtZKrGOVhJlnnjkMXyM2EVH8IdQoB5OhCAzbM7yN2LNA/HKizOJW+8mEKwIz/61MfLIOKhY/ZoozlI4oRnyut956wRIMF4S+rZOa5cU+zLB+xukiDknD+4CGeyVvxK8FxDwTpcoFXAYoc7ZTQIeA2f5YvjsbsvfCovQrrbRSEMrcE1ZQ1lbl3rkPBREQgdYhIAHaOnWlkopASxFgGN5PTgnLJDFMnA0MieNjybI8iCGGiv2EozCjm7h+wky4xIaWmfXOLyixLBLLD6277rrOT3QKM9uxoJLWcsstl80mdx//QSyOCE7yR8yybBFLILE8EoG1O1nqh1npnMePFYttowK+kPg2Ugb8ZbFGskyTn2ATLJQjjzxy8KnFbxNRyrA3bguUz0/8Cvwom5845Yj70EMPBasyx5hxPmzYsLBSACsBMCue2ehss7IAQ+3cL6sKIORYjop0EbblAjPnKQfCkGWy/EQhd/XVV4dVBmDMklGdDdl7YYF6XBBYuukD75JAvpdccklwIWDFAAUREIHWISAB2jp1pZKKQEsRYBgeEVls+J2b8TPeg2UPAYRYYTkkRBdD0wgZAssMYY3czC+DxE80MgR/1113BdGKWJxmmmnCkKyfGR7iV/KPNUfJh/LhO4hfIuuEIvwQsgQsqviv4gvKeYa+Ebixb2kleVUT5/zzzw/DyQhjysg6nlgpDz744JAMi9qzuD0WUDjgRsAQOMsmUU4bQt9+++2DCGQdVALClntDGMKL4XxEPH66+LUSEL7cO8tCkS5D3Ahus0KGSDn/qAMEMRZalpVi4hn548KAZZTPWkJ8L5SZ5aNwO0CwUxdYdv2qAh38dWvJU9eKgAg0nkAPP8yS7+nf+LxbOgd62yeffHJYp6+lb0SFF4EmIIAFlOFh1sMsFhh6xhqKVcwCw7xYAhE55YSSXZP9RLT95z//6TABJ46HjyPWUMrYFQH3BBbWRwjm3RfNNhZA7tsEZLZciGuG5xFtFrgOKzKC2iYQ2Tn75DqG07MTkux8qU9EKNZTVkLAUluvkHcvTHDCP5YfL1AQAREoJOCXZgsdTD6bNfx/S96sJVS5REAE2p5AJWInb0mm7IzqzoCyyT+lrs3Ohi8Vtx7ncE9AxBULiNJS57kuT2Byna0yUCxtrsu7tlj8+DhiuBafzziteDuvPHRWSnVY4uu1LQIi0HwE6tdFbb57U4lEQAREQAREQAREQASakIAEaBNWiookAiIgAiIgAiIgAu1MQAK0nWtX9yYCIiACIiACIiACTUhAArQJK0VFEgEREAEREAEREIF2JiAB2s61q3sTAREQAREQAREQgSYkIAHahJWiIomACIiACIiACIhAOxOQAG3n2tW9iYAIiIAIiIAIiEATEpAAbcJKUZFEQAREQAREQAREoJ0JSIC2c+3q3kRABERABERABESgCQlIgDZhpahIIiACIiACIiACItDOBCRA27l2dW8iIAIiIAIiIAIi0IQEJECbsFJUJBEQAREQAREQARFoZwISoO1cu7o3ERABERABERABEWhCAhKgTVgpKpIIiIAIiIAIiIAItDMBCdB2rt0mvrd///3X/fTTT+7rr792bCuIgAiIQCsT+M9//uM++ugj99VXX1XVpv3999/un3/+cUmSNOT2SZd29oUXXghla1Q+DSm8Em1rAhKgbV299b25H374wd17773urrvuct98802nE3/99dfdfPPN5yaffHI33XTTuSmnnNI9+uijnU6vO17IC+uAAw5wM8wwg5tgggncAgss4O64447uiEL33MUEeM5WXnllN9dcc7kVV1zR3XzzzV1cgvLZff755+7YY491W2yxhdtvv/3cM888U/KiP//80/3xxx+OT0Tk999/30FEfvrpp2799dd3vXv3Dt85GBD3ueeeczPNNJMbddRRXd++fUO7Rtv28ccfu/fee89de+217vbbb3fffvttQRnuueceN++887rRRx/djTvuuIHpr7/+WhCn1h1E8TrrrOOmmWYat80227g+ffq4WWed1SF66xVgdd1117lTTjklPAu///57QdIPPvig23bbbd1KK63kdtxxR/fGG28UnNdONybge0MKnSAw//zzJ0888UQnrmzNS84555zEN5LJUkstlSy77LJ01ZOTTz656pv58ssvw7W+sUq85TNc7xvncMw31FWnV80Fzz77bHLEEUcke++9d7LDDjsk6623XuJfIslBBx2UeAtBNUkN17hwm2OOORL/UklefvnlxL/YkhtuuCEwvOSSS4Zr2ZR5exO4+OKLw3N2+eWXJy+99FJy1VVXhf0zzjijaW78k08+SbwYTLbeeuuE8g4cODAtc7aQjz/+eOI7wyF+r169Ev68eEzGGGOM8P068sgjk5133jm0G7R5bJM+7YXvAIZ0Oc7fXnvtlUw77bRJz5490+O0mWuttVbaZt53332hCOTLNXxvvRhM/GhQGscL3WwxC/ZPPfXUkA9lHW200ZJJJ500WW211ZKHHnqoIJ7vpCZjjTVWMvXUU4c2bqqppkpGHnnkUL7xxx8/8YI7jU9cL74TLx4TLxATL6qTn3/+OT1fbOOxxx4L74U555wz2XDDDZN+/fqF+3rnnXfCJRdeeGHYP+uss5Lrr78+WX311cP+CSeckFxzzTUJbb4X8cWS1/EaCMw222zhO1pDEg2/FLO/QicIdCcBeuedd4ZG4/33309JffbZZ+FYtSKUxn7uuedOdtpppwSxRKNHePrpp5NJJpkk3U8zymwgtl588cXED3MlNJqVBgS0txQm3hqSLL744ulLgkZw1VVXDffSKg0h4p2XVzZ8+OGHibemJMOGDcueKtj/66+/gnC97bbbAkv2Ff5LAHEPD17O1kHKY3PLLbckm2yySeItgMmee+5ZlnleGq12DFHCc+ctewVF5zuJ0Hn44YcLjjdih2ec9uiRRx5JvLWwQxbeGhfK6K2fBecQOpR9lVVWSWgLuBaRxDHaIQQ12yeeeGL4vPrqq5MRRxwxbB9++OHJZJNNFrZfeeWVNF2ej3HGGScc33333cPnU089Fc7z8ic9/kzI0QFmn+8dnyZG0wT9Bu0iHeNi4fzzzw/X7rvvvuGT9pe0Zp999vBJ2r/88ku4tyuuuCIIVAwGlPO1114LzzVl5BqeXcKll16aeAtuuN8ePXokY445ZmLlP/3004sVJdlnn31COv3790/mmWeesO0twEHAk/5mm20WjnlrdOJHvZKJJ544WXrppZPpp58+HCeOCdL999+/aD6VnHjyyScTOguHHHJIcvfdd1dySdvHkQBt4yruLgIUkUeDQY89G4YOHRoalbgnnY0T79M40MAdeOCByWmnnZb4oazQEGFFwaIw88wzB2EUX2PbiIKNN944CCysfwitGWecMfFDTBal6Ke9fLzbQEKDSsOH1WHJJZdMFlpooSA4DjvssJAeFoBmD7w4br311txiIoouuuii3HMc9G4UiR+uT7zbQxDeiHJ45L3MiybSpiewPPGyxqqEpcgPVQahE9/ukCFDwveB5xgL9FZbbZX4oc3AkJdsIwLfQSxkWN6sw9aIfMqleeihhyYIn7xwzDHHBAtg3rl6HIMB31esfiOMMEL45LllBIBA+0DHlM4AYixuFwZ6CyiiCpFE2+FdB0J9TTHFFMnZZ58duJIW7QMdaxNWdE633377xA8bB4soonSUUUZJ3n333ZAnozljjz12WhauJdgoz0gjjRQ6ulhNGXVZbLHFQt6IWcqYF3gG6Yhnw48//pjsuuuuodwwoLzLLbdcsFgihHkWEd2IZtpGrJ3EYZtP2j3aPAtYb4mLeOS8CVssltYmILY5d/zxx9tl6SeCnXKsueaaiXfJCvG8j2n4ZJSJsnGtH3YPnTO2EcS0xeSLOIIPHZc99tgjxEVAdiYg/qlLjAvUNXlhie7uHWsJ0M48TS1yTXcRoAgTXsjFAkImtowWi0dDT8NAg42VYt111w0ClF7xeOONF84xfGMWhjfffDMIKXrnWCa9r2gY6mGYCSG7yy67JBNNNFFIjx5/qbDwwgsn3k8tREFwep+kNDoNNQ0ngcY4T2inkZtkY5FFFkmGeCGUF3hZIu7zAuKaOsAKFAdejLwUzVITn+su2wg82GCB+u2338Jt88xwjA4S4YEHHgj7HEPs8BwZT16udKiwBtYzYD1adNFFg4BiaBhLFha84RF22223os8WQ93e37IhxUI40RmwuuA5/uKLL0Lnl2NYvPhe810mHmKT44g27/8YthFEdLw4jhhDODFUjqBnaHjAgAGhA0p7ZNcjrKhnOuCILTrOWPDMWkeHALFGOqRvAess6SFW+SNPRCjPC88J+whD2kFGMygL1kIELiKY9tACnXssoog1ruOPPJdZZplgtWSfzjsuEGwjXo8++uiwTZvJsVlmmSU8N7glwA6XJ7ZNaPJ8EQ/hbYE2gvvE2o0bASLU+6uGZ5xRrAknnDCUgTJjPTWr4wcffBDaZUQo7TkCE3cn7wMaRqyoI1wGvO96ssEGGwS2uAiQP/ddqTHDyslQPtfGHQ6zTOcJZ7uuO3xKgLZxLXcXAUoDjQDNswzaF53h8Gzghe4nFgU/WV7o+AgxRLfCCisEnyQaDeuRI36s5+onOYXGikZq8803D40Ucfm74IILEnyX8NnkJWK996OOOiqbfcE+w0I0jAQawGHREPUaa6yR3HTTTeEcvlo33nhj2G7mf1h5eKFlA/UBp2K+yVg3eFnkBTice+65eae6xTGs68svv3yHe8XCxosaaz9sl1hiiSAiLCKWOZ5fRCHWHp7L+++/P8S359viVvtpophOhVlzGEalHLzguzpgJeM7khcQZtW64+Slkz3GM2sjJYgw2t24w8moCTywjhL8ZJggjPxkoVS00YklnHTSSQnPOfERaQhHhm6tc4yYIiCYbPgd9ohPngHSpP3gekQf9Wtl47mwgG8sQtCsj4hQ/FHJh/z8RKDUikscxKDlhxizoXGeLcQw1k3yJB5ikm3aQT7pjCL88b/kPFZq8kaI8xziy0rHBfGLgDTLJNearypiknvkOWaEhIBvJ6wJlJe4tN9YaG1ECSsuQphztNsWMEogLhHhdJgQ/pQF4cvIAffM/WKlZMQBEY7IpvxvvfVWsGojaGHG/ZIPVk7rGFo+8OHe+E5kA4YT7h1B312DBGgb13x3EaCIGhothhqzgWOIu+yLlt44vVmsEvSWaeywfNIY4PfEPiKKtAnmP8TwHo0d5+OeMHE5xh/DW3GgAaaXzfATIoKXx+DBg0MULLNYHLAE0GjSwsaDAABAAElEQVQSeFHZNvuU38+QZTM0gLw8ujrQkFJmhpCw7mJBKRWwSsCCRtkCAoWGH5+qYoGXKZMB8gLiEytFdw08d9ZJyTLgBUqnB0sV1vSslZwODOKVlyHPImKMlyzPfPwcZ9Mtt08ngxd8NvA9wiqFdbQrA+KEe0TsxAFLGc8jFr1igY4sk1sQIvhxVhIuu+yykC7tDIKLZxyRT142UYeJP+xbQLRjecNaiRsFdcCQOcKGeHy3Xn311SAcESh85/BJR/wYT9oR2q9BgwaFaxCt5gfPZB/aNL5r1hkgXf7MLYaJjXbMPunE2zYjNwgx9smDQBvKqBDHttxyy2CZRdSyTzn5RPzS3tFGUD7KwHHSwi2B7z7WWsSduWosuOCCIQ7ffZ4bhCbX2HVca+KXZ8qs/fj7YjSgQ05c/EZpd/gc5jvw5GfpWJrm+gBLzhkz+PLHfXPcLMzW1sOX7wnniINrlaVNh44RKjM2xCMMPA90Suw9EkBG/yhPPdpzxCyjSnS+ENa4OBXLM8p+uG9KgA73KmhcAbqLAIWg+TQxVMtLBGuQ+UnFDQJxmZxB4+GXH2E3BCYYcQxLJT1bZtJjiaNhpYdMY0ZDY5MCaCh5keAbhM8WLx8aNRonxFoczHpBmrywmCFKXvh50bOnwTCfL3raNgGARpQXKXEZvsGHiPJk04/zasQ2DRkiA+sEfHi5UKZyw5lYYIjHy5Z754XEcGO2MxCXGYFfzM+q0T58cTmacRtLlomabPkYosT/jo4YL3gEKR0be1YQVdQFli6GbS1gvUEkZC03dr7cJy9QhoDzAmLDLPd55xt1jGFt7hXrFUIJQc62+T/m5Uv7gQBBQPGscj1DsKWeVZtMhHjE2svzbQE3CaxjBAQh7UccbKY++XCO7z/tjHVMiYuVEMFFHAQUzz/1Z+0HghGLIOcRrIQzzzwzLTvClk4GVtXYNYP4/JkIY9smOdo5E5+2j9XT0qMdQjjDlPaISVF0fsiLNhNLrIk/rsN33SyZcXqhwP4f7SjnucaEIvHiMnDOBDJp0iZRZjibxZSJm7SnZvElDdKDK9uUlTTN5YDvAcYArrFy2SeCNzYq0GnjWec8VmiMAhtttFFik7b4rhGwQCPuY/HHSFm2Q8N7BIME5aNN5X3S2YCQ5zkhX3yCrVPE82ujEp1Nu9HXSYA2mvBwTL87CVAw4x+43XbbhRcwjSPD44i4ONhwvflUxudw5qe3OsT7LtKbpRHhZcbLgUaLLzNLkhCHbRoj/JDoidMoWYMZT/RAOBKPRpmGwQLWD47HvpAIOo4h0rgPtmmYaQixEiAAzY8IUY0gxiKIaGM4qlEBiywvqzjAkfKVG2alvAwR8wKsZAY/7LFgZX096eGTH0IqGzhHHZCPWVWycdphf+21106Y7ZwNPOOwoTHnObUXJ88pLyYsN3TMTATYM2TpYLFjGLkzgZdy9ruE6KX+EAZ8X4ZH4J4ZycAS5NeyLBgSz5bHBCsWOAsIT5giaoqJUIZ0GRnB2mjCCZHE0DmdYNogAunE4pQycQyLPsLKhqpjqy3ti4k4LKLER1ghphBH1LPVJ/EYaaDDTTnwQUcY0f4hDC3w3bBZ3yZcabP4Q4RZeggm9kmLjgxWRvLHuobAMf9NjiHgaIcQUTxvCHmsg8aDOPyRB8KVOIz2YGGkg4rgNkMBxxCJdg2f5G0CMj6OqGcftxI7joUfRsbNjsef1kZzjI4/n/b9sbzterO6xtdzDisrYtw6d7gTYSmlzmiLEOJsW6BuaNMs0FbDlzToZFiHB3/dagNl4DtIJz8bOEaHpJmDBGgz106NZWsnAWovNYag4i93tYgY8qaxKBasEaYxRBDSy6ZRM/9SGiPEGI0X1s440Mhwni8+jT29XhpwGjIaXZtYxMsRCwKNOefjgLWChgl/I2alYvVgMhOzmI877rgwtPrdd9+FlxYvrPPOOy+IEvKNxWycZi3b5EXaeRYyhgx5WVlDXEs+8bWILPJEONAom/Uub3YzrhSIe15UWF+47vnnn4+Ta5ttJr1xfzbBhBuzYwglziE0EZ2IEPax3GAJYRtOWWFPGrg8dNa1Aasr3xOz+DDyQLtj1jW+E/bck9fwDoglBBLfb7jwnaITNcD7AuYFrKKxxTiOE4sfrIAmJGBNO4LIY3SEfQQoVi6GW9lHxPCdYhvhNPB/M6PpZNL5pX1A7JhljPIx8sD3gXaD6yg77YqJqk033TQIXysj6SIQ6Rxb5498KRdWVOoIQYgbEuVlm+eF9seshLaGKmXhuaODiGgkT9o18qS8DINjmaRcPGd88tfXWwr5JD4jO9wf+dB28YkA4TzD9nZN/ImQpb7Myhmfo4xY/vElpfzUKeXkj3j43lp7TjpWLrZpu2GM0cGEp6VtPG0/+0mHDZ/XONAOMjn17bffDuxo4y3QgbHnwOoZqzycrTNIO893JTtaZ2kU+yQ+gjevk8RoSfYdVSyd4XVcAnR4ke+CfNtFgNLo0kDxRaPHSEMyaNCgThFkZirX2wszToRGg0aZBpbhFRoeXui2fhuNDI0WFgs+s0N6NKhcQwNI44xwxUJqjbCJWIbsaKwRqJyLA+XCisLQJb5MWFhIj8bWrBbkwdBiHGy4u94TlBjao4HPBl5kiF8aeNYozBM22Wuq2ccih2WJRpmXFpNLsgF/NljYgtKcp+441kyiJ1vuWvbpgPH8YUFhuJgZ0YgJrEQ8j3R8eL55FhmSswkpvKiZ/JEXmImLK0k1gUk2fEdsiJgXnQ39IXqoA4Qt4o3tK6+8sprkGxLXlvHB3YbnlY4KZcMyZytQIJK4L8QkQ+j4duZZ+U08cr1NIKLQfC85xh9CBkFHPrDgGL6bCHbrVJkgwZcSlvH3nPaDayirLTdkvqwIfZtoQxzKT73b8kvmusNkIfPbpANHvSG47EchyIPvGeKNdPizZ4Zt2jHKhGg7+OCD0ziUlbIg0E08Zi2VJgRJh2eWstBZpX2LRR5tHNfaMa4jPysPbSTMaHM5xjnKheiibeYY7RD3ZdZ/jplPJuKdPGgbqQ/iIhQR75aH5W37lpcJZDuOYMSAAS/zx6aTTplIm2eJuNkOHe067yzcYxDUdISwlsYBlxXiVBPw8UVoFwvUnYncYnGG53EJ0OFJv8F5t4MAteExllexXp69OFjeotpAGnxh82bD8qJhuNnEKUMz9PB5ydOo0EBgAbBJBQzNM4ue4WWGhGjESIO4DKEz1IYVguPx5AfEIg0uLw16whYQDzTSXM/MS2sUGeqjkeNFxzn+6DFnAy8k8qtnwALDy4ayWeAFzsuOIWFeJta7r7b3bul15hMxDwfzfYvTwOLDs9+ugQlpPF9YYnjh4oPJy88Czy+WNp4HhA5DwbzY8PWMLTPEZx+OWJsrDTwLXMNEJgQxosZmPvMi5gUdTySjfAhU64BVmk894zE7mjJnLfmMpvAMY8nFokj5+d7ynTaxxwhENiB86EQijkjXrO60LzbMDe/4e8NzSYeK7z4uNbaiAwIT6z0dbKxi+DXaZCMs27Qt1CnD23RCEVAICwQWohq/Q8rAPsPuNsEo/tUha0fxx6Rtwaeba/hjuJ465N5tNjvHKQ+ftEMIPtpB8uYYbSH3QHkINnOfc/aXtSxynPJhDWab+6EtQVyyDy94WkeeY+RJ3rEo5jh/8OMT66j98AVCFzYc536wcuM6wMom1K+JW+rc0uHTfPDZpg64Zzq+7Fs7zLU88+Rr4hYrI3EwXCD+2bZ2mnvlOUKMMpJGJ4+OBp3kvEBnhPqpJlCv1FOeAQA3EDoY9R6hqqZ85eJKgJYj1MLn20GA8uW1JT/iquCLjzBCzFUb+GLSUGCpoaFneAo/HRqY7BfZLGo00HHgpU7DSMOFlZSGAwuQ9app/OgNIxSxEtBwWeAlRY+cMtiMTl4wWFgpAy8cfETZNkuG+QcxQYe0aUBjUUvaWEnyrJWWb2c+KRdCBwsaAcFHubEw8mmWMwQRL4ksv87kWck1+NliTcgLiApeYrGrBv6hLIDPi4OXOq4OrRiYIEf946aAoDFrIwIUq3Q28NwiGqgXXDqoM3zQ6NCY9ajajhw+e+bfaPnxkkNY8TLMmyiFiwRCZ3gFvk/FLMC405ggYUKMBRtVQJDY98/OISwQ3bRNLHiOsGR0BtHFd5tr+P5nA3WWXTWA7xhtGZ1f/CsR6ohghBmuN5TNykVZGXFASG/qO8eIfYaRreOKuCPvvMlfjLjQccRSznMQ/yGmac94tuw4+3T82ecZYp/7ZJ96to45ZaGdxt/UrjXrLfu0fZTTGGMlpt3kvukg4YaExRnhSNtIu8d329Iy0Uv+HIOviVTqAOs68eFKGWBJ/mbFNLHItSYmsdpyrwhuhKLlFX9aXI5RBhO2pIv45ntgcag7nn9GIwi0PdQFeWDtxLJOx4K06DRaoHOGawXfY+7FrrfzlXzynOD2BU8L1rFEdDdzkABt5tqpsWztIECxVsaTemIk9GrvuOOO9BBDDTTgNAbcO42n+VClkf63gfBkyRpEH714hF52piJR+XLnLapNA09jQuNBsAXBOYaYZXgF3yr2rWdMI8/LHssKPXXO0Thj0bLZqxzDamK+o7zYWeoGqyuBBpuGDmtBdvYxDTFM6h0QMZSLlw9im4YeqyeNeBw4jn9aVwSY5jnekzcva4bIeJkRbCkaZp3CDBHH/SBEm9k6EAof/TPLP2I6DnwHsMDQ+cneD/cZd+B4GdJJ4eXIZ6UvPDo7CDi+W4hdRE92aA9BwvPHounZQCeMiWLDK/CdLCa0cfcwccSEKsS5LcUGHzourO8bB8QpHBB7sIEFbQoWKTqdCBushHR2WQUDizAdRMQl3Bn2tsDzaoKK9Yq5lu8S5bLF24lP3fLrQLRrCD7qNu5k8d3kGAKpWKANGuZHdrB4IyIRSNwHn/yZ2CMd3Dz4xHpNOwwXykB5TXgxAYay8/wRlz+sffBEPCN4OWbiDUsz7S1tGJZYhqMRXjbUzX0h5CkneSBGLd3sp6VJ+8TIj503yyj7JmRJi2eQY4h7cznA3YJ2n44DItnuy9KCkW1TFrPecox75j65R0QvnSxzx7DnJ7uiB88G1zJxiXcI7Hkf4Jph+WRdu4rVpR2nnbMZ+ghwhDGW5Lx1mO2aZvmUAG2WmmhAOdpBgNJA5S3iCy4aHRYOJtAwIpAQbjSurINow0LmgB8iVvmP4SzLI3up/YIJL2UaD15GNLBxoBHGl4wXFEt1YIXCeoXo5GXFi5GGkQaDT44R+GLS2GHVQnQilgi8wGi0eFHQUOHTikXElkaK1w8NF9TpH6IYIY5lgSEvxG7c4yYbGlI6AF0RePEiQPN8C3kBIrooH+tCUjd5wpjrm91CELPEcoIlLy/wnNnLFqGNhZfvApbrbD3lXV/qmFlT8CXkxQo3Om5wjS3eCC7EFXXCc4tYoi4QpcSt1xA8L1wEIMuoZV0Kit0HlkesbBbwAYQZ1jREBOIZMUJ7w/cOMWGrVsA8KyRobxCF3Bd/dCi5XzoDdgzhyveF0QEEO8eZXGj+owgXhs/53vN9ps3CMsj32wKiD+HDMdYD5Z4t/djlxYQN7jqIKLOYWjp8wo10uA6/adochJ6lhyhEbCKATfgxDG3lRZAO9MPEuN5wDWnBkHaNNtBEPPHYNlGEiCM9ymUWVMuTT8vLxB/D1PCNxZ/FRyRb3mbhRADjj4n4ovxxmnCNj1HPdPq5FgHJvcLDym5uA5a25cunlY9t3gsWh+8bnQ+re9wB8B3GvSpvFrr5zJIOnSJcPEibOuEeqBd7D8T1V26b9wQjcbTBzB9ohSAB2gq11MkytoMApeHmLxvotfIFNgsnwxs06NmA4zw97NgXKxun1D5DzAirvMDLCssIfp/4cOVZUHnR0ciVC6TDkJoFGjCGjrA84DzPi5yA8OK++eNlTyNNHBpDGxLrTONl+Zb7ZLjKfNeycREgg6p0os+mUc0+jSwc4M8LF+uRTUKwFzCTYMx9IJs2w6ixD272fLPtI1bMZSNbNmZhYyVnYW0+6dhgYTErcDZ+pfsIAUQIFm8LWKlInxc5YsQELtZl6sMmvWCBQeDb84oQqzUwFE55EImIJ6xg8c8Z4jJDhw+/P17GdNAIdAARd7QRJuLotNkoBWVELHK/2cD3i18vigPWOwQMItPuj0+EBEKO9oj0EG8WTMgjVPke4/9Ixwg/ctophD3DzFiKYckoCmlS76zewfcbnuYPCQNYY/nEAkdHmftE0NH5zgbaEKzkPBPkb0PTtCGkZR0F8sTazXH4Ur9cwygPZec894ZQsslV1vYgPhmeJg6inU/+TGTafvzJfcf7cbp2nOtp42ySD/WYvc4mI1obwDOLCKYzQQee7zrPAJ10OhWkQfowQYByvxgRLM/sp4nU+F5IN+7YYA3HgmsuVljAs4G8cWFBaGP55Dkc5q3SFvheDa/ly6wMXfUpAdpVpIdDPu0gQM1fiQafRpPAMBmNQzwsxvCJzQDNoma4miWDOhPs5w3NGmJpMHxPGXjBMKRLo5EXeHHQUJYK+FXysqQnT1oEGlPStwaRYStErjV+vLQ4j38mDZr1eHnx4nifnWFZKv9qzmFhoQwm/O1ac0mwF74db/QnYoIXAENqvFB48cYWbyxf+M3lhXIzSPOuGZ7HmMWctxQVZcLiy3BtvQNCHqGFsLEAX549hB4vbb6XPA8cM+sYzzL1wXcPiygdQNLJW83A0i33ycudPBDYJhRNTGI54g+rHFYnOo3mj42PJoEymOUWAcA24o0Omy1nhYUyDjbBJxaS9kMWFo+OD+xpA7hHhBzl4DuMcOO7ynA6qzYgSBCoMU9Lxya9wM2GvxFzFhC3WFrpZJC+iV+2WW0DUQgfazN4uWNVRfgjMDmH4KaNgYVZEzmOaOKP+iQfzpngQvDS1mJVx6JI+RFNiCcCAtXKRlqUB0HMNoLahrtJm+eC4/zRYaUuEbkIQyu3nSd/2j2uM7Fo12PV5J5skifXsA832ic6ALhP2HUMuzM0Tf1QB5Td2lLLzz65F9vmk3iDfMcaYwJWfI7BAaGMocDuzyzSWIWJU6zjiyCmjS72a3J07rCidocgAdrGtdwOApTqwceHxpnGgwYFsZm1wtFIES8v0Ihnf5owL16xY/YTeYhghthJDzFov0lMw0OjzDBMNmAxZGivWMD5nEaWRbBtKJ98EHL2i0g0iDTqNHp80rht6q2lNgEomzYvVQRhowJ+hJSBdUexOJlVJG9GeqPKUGm6+EvSATArXXwdQ1W4UbRKsOcw9num7FYfzCqud2BokxdtNiA4EQg8m7xEEA+ICSanYXVCLPJ9iC2wWKOx+nQ2MBKC0MoG8jKxhEBgeJRhbYal8aXkWaWTgmgdMmRI+O4yQxzxGpePoWTi0qnjXvgeIRTse2754oMZT2KzeuH7iC85eZMOwpfhViyoCBXuneMI3+xoif2iDlY+LIf8IfqIj4sNzyrbiH3uA2se+wgw2gQENIFjCDpEMYKLIWVEIJZSVkawdT0Z+jWrJfERz9b+kAYdB4b/TYzR4aYto3w21Bz75mOZRKhRTrP+kQ7tJJ/80Q7yycgRn8ae5weXJOrQ3JoQpLiU4CuJYCS+CWwY095yDCFNPSNIraxxfdHJREzDHPcnSwOxyveFOjPhy/sFcYjgRkBberxbEJ8EmxTKOdJF5FIfdAZIh7h8mnWZYfE4mCsGHeVinTHedbKAxtSG7/b//4ju8C1Hy+XeLgIU8HzJserh+2WW0LhCaPTzFoy2YSyuqyXwwmXohpc9oivrz2YvCHsRIHjsGC+DvGDr6sVpMdxOw8qLA2sHPX4mn/DCNIFLo0mvG0tIXsD6g19SIwO+gFjAEAS8kDtrYW5kGUmb5waLFC+GWIQO8UIEztmJXI0uT63p2zA3FhyG3XmpIySygqbWfOx6LHW8jPMmRiBA6BAyImFuHwgcXtJ5ge8G4qOzAZeT7AQsSwsBguCy4f+4rhGBiCNEJdZLVpnICwyD0rlFOCLQ6MTljSTQaTRrM99JniPzRUaQcB4xw/HsTHgTuQguC5QVEUb7hQBDAFlAmJpowmJogXxInzYewYxbBJ+0G4yCYAW2iY12jX0yKoPoRExi4UWMsXIBbQrXsIwU4grBCg/raPA9Qlwh/lhBIQ5Y++ggUybqn04A7RdCzf5oK7Cy4jvLMasHOiw818aMNOiAUx6rbwSZWT85Txr4H/PHPhZKPm05LMrGfXKMZ5JnmHtkghzHSIs2gc4p7RfpYRVnxISA8KWMdk/UIx0x/Kq53spK58eCvaPwcyVP6pi4jMAwwsUoBcIeg4VNUopHa0gH1weuse+Tpd2un7KAtmvN+vtqJwFarprw0eKLy0vEAj1sjvFS6opA480LGd80hoJ4YZpfUjZ/RDQNe55w4BiNPJZRrDR5gWsRuHkBawUNssJ/CcAalrxMsOLwIuBFytBoKwaskgzRIagQQo12e2Bom+9R3FHixY/oMyFmHHEFYeg7L2DxYQJbZwPWqWIz6bEC4gtKHccWMPJCUCHYzFKINQyxkA18f3k2ygXEJtZRAkO7JqTYR5ggYjiP6EUYxgF/cEQPFlELDO8jBgkIftoQBBL3g38zYhDhFk+GZMSEDghD4QgjxBP+odQRnK1zWMyShhikTskPFx624UKwpef4juA6YOXD6ku7Yx3hEPl//2z9YzoZlAmLKgISDqRNvWOpRMjZMY7bJCgm4iCKOUYdIsrNrQcLMKLN3J4oA+ISdyDi83yyrjPbWEQt8P0w/ghHnk1GsPjuINZjtypbJzZ2pSIf0rQhdcpKvXAPiFPq13zzLU+bYGTlQGAyWoewpGMTC0vyt/Jj7eY5pQPR6O+zlbUZPiVAm6EWGlSG7iRAQWjWBfy4WGuOhpnt2BrSINRpskw0QHRmhx/TCP/b4DyWjWIBqxYvpWKzfBmeYmgtnoVMWlhRaNTyLFbF8uoOx3kGsCaxTAqTKYpZpbsDi87cIwICSxnChqXJeD4Z0swGxD4uKrQ9cWAIk+cSy09nA75xedZVBDlpD/QztPP8rRFv+Nsh7BjJwJKIqIvbBYQtaWD5KhcQF4hh4jOKQb4IP4Q3HUfErQkiRBXCkdEKLJNcg/UdX0wLiEDEjokWrmckhMk0jGYgBG3pJbsGtxesagSEnvmOs4/Q41qG/LNDwJwnUIe0HzAhmOXYfF4pp9Uv5UF4UU7YZTsY3Dvxzd2B/PmVNDrhiElEl60fjMAjrolNtm1Inm3+eLbshxGotwEDBoRyml8scahTnjE6ACZEKWO8Ygpl5buOIYL8sIAyeoR1m5VIqLs4YEFHWPIM0b5SJ2bphBUdLtyjrO5tNALhjqBFmFLWYq5gcV62jZWUOqZDQoeiO4lPGEiA2pPQhp/dTYBShfQ46VliBWAIqVkDjTZDQHxmAxYHGj5m7/OyygZzKzDfS3rPvNh5IdE4x8NQ2Wu1LwKdJcCQO8KC543JecWCPZ+8mLFOYYXC+ljMGlcsnexxm+jE98KGxhGMPPOIUz4RDbEAYGiY47zY+b4hRhDJWCI5jhBDqGB5KubSki0H+4gyrF0IFgQgaSPirMPI7HaEGpY3/DQR8Ph1IsYQlwxRW0AIYxXMCiLOM8qB8EE8UV4m1xBMUDNETkeV8hBsSBrhiQUwuywcccgPYWiTdthGsJIOQo2y0jk2H03qG79OAvdHHHjh5oO1kzKQF4GVJUiLjgoWU8rMUDziDnELL/LhePxHmrR5iDCzwCIeLZhrEsPZpMG1+OdSjwhW9rFUMupkLHju8FNHrFIXHCdthvxxYcHiHQfS4BnFgIDfKRPwzI2A+sXKTGeAeCbs6TQgRPGL5fthzOJ0tV2cgARocTYtf6Y7CtBWqTReAgzd8XLKBho9/K0YeqKxi4e8aEQ5hjWFQIOKHyCWBWa7NuNEoOz9ab/9CfBiZtY3Aoph+lKCtRoaWOEYPkYQmCjACkYwNxz8/RgSxiLHJBjEKtbAeNib+FjEGWLGt7uzlieGghE4rNEZB+6d7ynpx8G+v4i6ONiPVWzqJxcy3IxQNSuq+QmaAEd0YXFD+JIHQ9VYgbGCsm9CluvYx8JmAYEEHzoH5keKWKaezPqJwEPwEcxX0XwxOcY9INSwkJIXw9fFAvkxKoM7AUPRiDtGH7hf0sQnF5EIp9jdCMsgFk+EMAKcemSCG+2mWZKpU95xcEAAcs5WYUDIIr65F/wuzbpMHCz0cDE/XOoeyyVuAJyPA+XHWsqkLyy1WOCLuVXF12m7MgISoJVxaslYEqDNXW30sGkImXSA9YWhOl6uWCSw0hD4FRXiMMuSyQ3EpQHONpTNfacqnQjUjwDPPt8PRgoQQ3Fg1ANLGt8hLGCIX9rBYkvixNd2dtuWikMQMbzL9xTxxA838N1FDDMqwaoWiD9GLvICwhOfQiyTWIwR0NlRHAQ47gK0Fbg14FKCOEJkMax/8cUXFySNuKIMWADxb0XM0fE1QYYIxSKJ1ZP8sGwSn5n7DEMjAPMmdxZk0qAd6pZ1X/PciYZ5X39EMmVjtn4c4Iz4xCKKYOR+sNwjpnEPYB/XLKyZuEzwSfzssxSnqe3GEGgFAdqDW/cPjUKVBPzwj/O9aMenQnMS8C8C553inZ8Qw2oPzvfEnZ9g4Lx1Jy2wtww4Pwve+eF652drOj8clp7ThgiIQCEBL1CdnyjkvGXOeSHq/PC287PLw3ZhzPrteRHkvCXVeUuk89ZF54Wk86LQeQHpvPXPeauZ8/6Wzos/54Vx/TKuICXaGP8zoM6vBOK8EHW+E+u8ZTC90ltWnbcGh33eFV7AO7+qhfM+lc4POzvvL5vGbcUNbxl23srrvJB1fl6A80shOb/Mk+M58e4azotu5ycUteKttXyZvTuE8y5zjs9mDRKgnawZCdBOgtNlIiACIiACIiACDSXQCgJUXZOGPgJKXAREQAREQAREQAREIEtAAjRLRPsiIAIiIAIiIAIiIAINJSAB2lC8SlwEREAEREAEREAERCBLQAI0S0T7IiACIiACIiACIiACDSUgAdpQvEpcBERABERABERABEQgS0ACNEtE+yIgAiIgAiIgAiIgAg0lIAHaULxKXAREQAREQAREQAREIEtAAjRLRPsiIAIiIAIiIAIiIAINJSAB2lC8SlwEREAEREAEREAERCBLQAI0S0T7IiACIiACIiACIiACDSUgAdpQvEpcBERABERABERABEQgS0ACNEtE+yIgAiIgAiIgAiIgAg0lIAHaULxKXAREQAREQAREQAREIEtAAjRLRPsiIAIiIAIiIAIiIAINJSAB2lC8SlwEREAEREAEREAERCBLQAI0S0T7IiACItAAAl9//bX76quvGpCykhQBERCB1iMgAdp6daYSi0BTE5hzzjldjx493GabbZZbzi+++ML17NkzxPn4449z4+Qd/Oabb9wll1ySdyr32D333BPy+OCDD3LP13LwpptuCml/++23JZOhzNtuu62bccYZ3UQTTeQmnnhiN9lkk7mzzjqr5HWdPVkto2L53HrrreH+qKu8wH1Tx6eeemreabfjjju68cYbL/dcsYPbbLON69+/f3r6qaeeco899li6rw0REIH2IiAB2l71qbsRgaYggMAcPHiw+/vvvzuU5+abb3b//vtvh+PlDiDkrrvuunLRmub8jz/+6JZddln3wAMPuA022MA988wzgcniiy/udtppJ3fHHXfUvaytxigGsNBCC7lVVlklHOK5WXDBBd27774bR9G2CIhAGxHo1Ub3olsRARFoEgILL7ywe+SRR9zDDz/sllxyyYJS3XDDDW7uued2zz33XMHxcjuIVqxurRLWXHNNN3ToUPfiiy+6aaaZJi32Siut5D799FO33Xbbuddff92NNdZY6blaN1qNUXy/m266abqbJInjT0EERKB9CcgC2r51qzsTgeFGgGHm+eef3zFUHQf8IBGma621Vnw4bN9yyy0O4crQ7aSTTupWWGEF99Zbb4Vzu+yyi3vooYfCkCzi9fPPPw/Hf/nlF7frrruGIe5pp53WHXDAAUHcxYkjApdeemk3zjjjhDJhmY3Da6+95pZZZpmQr6Xx119/xVGCtXK55ZZz448/vlt11VXdl19+WXA+u/PTTz+5Bx980A0cOLBAfFq88847L5T1zz//tEPh3gYMGODGHntsRzn23XdfF5/n3L333ut23nlnN8UUU4S/vfbay1lZ8xgdeuih7pBDDglp4QJAmgTKv/nmmwd3gAknnDDc0/vvv5+Wpd4bRxxxhDvwwAPdVVdd5XDRGHfccd3yyy/vPvzwwzSro446ym288cbut99+C/XEicMOO8yZMIUpZZ5kkklCXWFJfvrpp9PrtSECItBaBCRAW6u+VFoRaBkCWAARlbEli/055pjD9evXr+A+brzxRrf66qu7WWaZxV1wwQUOf8AXXnjBkQZh5ZVXdlNNNVW4juHrMcccMwzjM7SNyN1nn33cfvvt566//vrgfxgnvsYaa7jZZpvNHXvssUHQrb322u69994LURC48847r8On8cQTT3R77LGHQxzG/qsvv/xyEMz4b1588cVBAO2+++5xFh22n3/++XDfCyywQIdzHJhuuunc9ttv7yaYYIJwHivpYostFqyh+LniQ3nuuec6ym7hpZdecltuuWXggtBGwJ100knutNNOC1HyGOH/SpkRfohwBDSCdamllgoCGWF45plnBtGO/6UJe8uzXp8ITe6LOmKY/eCDD3ZPPvmkoy4sEOeNN95wI400ktthhx3CYaznFgfhjTsD155zzjnhPKK8nB+upa9PERCBJiPgXw4KnSDgrTvJE0880YkrdYkItDcBLzATLwwTb1FjDDXxQiO9YW9pTI477rjkmmuuCec++uijcM5b7xIvqNJ4bHiLWIjz/fffh+OrrbZa4q2iaRzvSxrOe7GXHvMCJRl99NETLyyTu+++O5z3VrT0vBe14di1114bjnlLbOItrsnvv/+exvEiNsTxojAc82It8ZbZ9Dwb3goa4vhJPwXHbef4448P5/2sdztU8tMLqWSuueZK/BB6Gs+L9ZAG90TwQ/WJF4kFceabb76E8lnIMvLWw5DGm2++aVESLziTEUYYITCyg966mHhxmngf0nDI8vaC1KIUfHLf1O0pp5xScNx2vIBMvJXTdhMvnBPvF5x4a3R6zAv+kIbV79Zbbx0YEMFbfsO5QYMGpfH79u2beOGd7ns3hmSTTTZJvAU7PaYNERCB/xLwne7Ed1qbGocsoE3WIVBxRKBdCEw55ZTB2smkI8J3333nhgwZklo14/vEinfnnXeGQ1i0GKZ/5513wj5DsnkByyRWUS/c0tNLLLGE+/nnn93000+fHot9ULG+jjHGGM5mxuOjuuiiiwZfTKyW/DFU7QWa8x3MkAaWR6yzcVhvvfXi3Q7bo402Wjj266+/djiXPeDfEO7ZZ591G264YYGPK0P9+Ic+/vjj6SUMO8d+sAzVMzRdKjBkPcMMM6RRmAw1zzzzFDDCouzFa3rPaeQ6bkw++eTBtcCSxApMKFd+i4+F9uSTT3ZYn3HHoJ4uvfRSN/PMM1sUfYqACLQQAQnQFqosFVUEWo0AQ+gmQFnahyH2qaeeusNt/PDDD2HYtXfv3mFYGsGH8CMg0PKCt+oFX9HsuVigcY40LXCOIV5mWSNs8UnFLQC/UvtjWJfJPAwJI5pZ2ggRFwd8XEuF2WefPZx+++23i0YjbwL+mJQlL02OffLJJ2ka+GvGYZRRRim7ogD+onHA1xMxmA3ZvLLn4/1RRx017P7xxx/x4XQbUWnuBXYwuz/yyCOHU//8849FKfl54YUXBr/gM844wyHEqRN8bKkrBREQgdYjIAHaenWmEotAyxDAhxFLJrO98fPMm3zEzTD5xA/Lh0kyWDYRffhjlgpM1mGpo2xA2MWiNStILT4iCgGHRQ1LZfbvyCOPDBOX/JC+88PEdln4LGfZxOeUgGUzL9x1111hTVBEFRNyKGM2D65DAGPltVDsXux83idLYsUBP9BK8oqvyW5j4YVdsclYrB8al5vrO1P2OF848QyxmP/ll18eJiodfvjh7vTTT4+jaVsERKBFCEiAtkhFqZgi0IoEZppppjDUyyQYJpDkCVBment/zTCbnRntiDfEyiuvvBJu2dYS5Vhs7WJYmXUi40koiF0mC7EIfblAeqSBBRRBZX+IJ4bYmRjEUDzWzEcffbQgORZJLxUYOl9nnXXcMccc45hlnw3MTCdtJhJhCcQqzLByHJiQg8AzMRufK7adZZQXjzphglc89I1gxx1h1llnzbsk9xjD+LfddluHTgAdABaQz7N05yaUc5D7IFh9Y2llwhlWdFZJ2GijjcKaqojcYiI/J1kdEgERaCICEqBNVBkqigi0IwGG4f1kleD/Z35/8X0yJM7wN0sMIf4Yjr7yyivT2d1+glCIju8mw+6IQY5tscUWDuvkuuuuG2ZUM7OdZYZIiyWTKgnMJh82bFiYdc71zIpnpjnbLBdE2HPPPYPljdnkiDZ+xajYLwDFeWLdZPibWd/4LuJSwExwZqCzBioWVht2pxzM4Gd2N/fP0lEsOYQgZIH2SkOWUd51LNeEqIcfs96xIsMN8U6elQZWFeB61jXFIsmSSFdffXW4P6yV+++/f6VJdYjXq1evIMwR5VjEsbYi1EmT2fMsv4UY/cDP8mfpLgUREIHWIyAB2np1phKLQEsRYBgewZhn/bQbYQ1IlgdCkCFeEGL33Xdf+MlOW+vRz3gOQ8dMGmIiDUPwDGUjWvnVHBZ7x9pYzc9cssTP2WefHYQT1zOhCVF72WWXuRFHHDEUj/Kz3BHL/5Anfoe2nqaVP++TiT1YCJkExf0gaBF4WDbZZ0kiCyz7dMIJJ4R0EZFYXVm3FAbVLFSfZWTpx5/4TmJxxi0C/1gsikwAYzkr2FYaYM4vU1FvfgZ7GBJnaSn8S7nvPD/TStMmHmkhaFk+isCaoPykKevDwpZJW6yDyq8/KYiACLQegR5+6CXfw7/17qVLS8z6flg1iq3z16WFUWYi0CYEEJMMhRcTXTRX+C8imuLAsC/DtIgeG76Nz5fbJl1+l55JPjbBJnsNcfyyUcGqyfB5teGzzz4LZWOR/WLBJj8hDG2STrG4xY4XY5QX3yZCZSc35cUtdQwRChtWPugMm2Jp03GBCZ0CC0xaIi+/LFNd87L09SkC7UCATiwdaZsQ2Yz3pJ/ibMZaUZlEoJsSyM42z2JAXGbFJ3FqFVCk26dPn2x2BfvEQfR0NsSz8YulgXhDxNUSijHKS7NWbpYm1uJafD4tnexnXmeASVW1Msrmo30REIGuJ1B9N77ry6gcRUAEREAEREAEREAE2oiABGgbVaZuRQREQAREQAREQARagYAEaCvUksooAiIgAiIgAiIgAm1EQAK0jSpTtyICIiACIiACIiACrUBAArQVakllFAEREAEREAEREIE2IiAB2kaVqVsRAREQAREQAREQgVYgIAHaCrWkMoqACIiACIiACIhAGxGQAG2jytStiIAIiIAIiIAIiEArEJAAbYVaUhlFQAREQAREQAREoI0ISIC2UWXqVkRABERABERABESgFQhIgLZCLamMIiACIiACIiACItBGBCRA26gydSsiIAIiIAIiIAIi0AoEJEBboZZURhEQAREQAREQARFoIwISoG1UmboVERABERABERABEWgFAr1aoZAqowjEBIYOHeq22GIL98ILL7i///7bTTjhhG7llVd2/fr1c9NOO61bdNFFw7H4mnLbf/75p3vqqafcRx995CaffHI3//zzu1FGGaXcZVWd//XXX90999zjhg0bFvJYaqml3Pjjj19VGvWM/O+//7offvjBffnll26KKaZwY4wxRj2TV1oi0C0J/PPPP+6GG25wjzzySLj/JZdc0q2++uquR48eDeHx+uuvu0cffTSkvdhii7kZZpihaD4///yzGzx4sHv//ffdpJNO6ohPu9mzZ8+i1+iECDSKgARoo8i2SLq//PKL23XXXd19993nEEhzzjmnO/HEE90cc8zRlHfw/fffu+mnn95NNNFEbqSRRnK9e/d27733njvvvPNCeeedd1631lprhft56623guCj0Z1nnnncPvvsUyBMEa+ffPKJ+/TTT912223nevXq5WabbTb39NNPu7ffftt99913btxxx62JA8IWofzmm2+6gQMHuqmmmsr179/f3XvvvW699dYLZefYY4895i699NJQHsTgVltt5biXRoUff/zRrb/++kF0w5CX2L777uuOOeaYhr0oG3UvjUr3tttuczfffLP79ttvwzO38847uz59+tQ9uyRJ3AUXXBCeCZ7Vueee2+25555uvPHGq3tecYJ8l84555zwvI822miODtGWW24ZR9F2lQSoSzqv/9feeYBJVaRtu0BQEUUQFBQVELOYs6tr+EwYMWddE+b4Gda8JlbFnNcsGDGsEbMYMbKKYg6AqKCoKAbAVH/d9e/b35lD90xPz3RPn+mnrqvn5Dp17jqn6qm33qpp376923333R2NvO22284tu+yy7o033ohCjzLhiiuucCNGjHCsr7322jG/O3ToUPTdiJf389RTT41icq211opC8qCDDopxUYanwzfffBPLlMUXX9xNnDjRvfvuu7kG/CmnnOJ4vwuFCRMmuNNPPz0KXcTqBhts4M4991zXsWPHQpdovwg0TCB8MAolEAiFjB85cmQJV1bPJaHw8+EN8ZtvvrkPIs6HitZfcsklcd+TTz5ZFQn9+uuv/UUXXeQPPvhgHwpb37lz55g+0h3Esg9izYcCMe6ba6654vLqq6+Oy7nnntsHS6m/5ppr/A477BD3jRs3Lj5XEJh+qaWW8kF8xf3E949//CP3zAMHDvTEFwrt3L7GrsAzWBd8sKTm7sF9gviLUZEutkNlFJc85yOPPOLPOOOMuF2uPJg+fXqMf6uttvK//fZbTEuwhMZ9hx56aO4xgyXHn3nmmT4IIh9Est9ss838iy++mDvemldCZetD5ep5l4LFyIcGQeQTGhLN/tgbb7yx79u3r7/jjjti/pMvvBeh0m/2e1mEoeEZ7xFEkg8i299yyy1xOwgZHxpmdlrNL2FBmRh6WHwQXT400vzFF1/sd9ppJ7/11lv7wYMH+2nTpuU4BbHpQ09C3J4xY4bnWyN07949xsM3FXpsfOil8ffdd58fPnx4/L7I79Dg9UFY+rPPPtsvs8wyPvSO+JVWWsnffvvtMY7kn/79+/sePXrEPCNdXE85GBoRcZ04kmHq1Klx/+GHHx7Lo9C49ZSBf/nLX+J+ru/Vq5cPjfbkZXHdyoajjz46Hh8zZowPltN4Xeg98aERH99byoZgxJjpeu1oGQLBmOLffPPNlrl5kXd1RZ6n01IEWoMADa1l37Nnz9STeR9a5j5Y/jyVVEsGCrdgBfJ77bWXD63tKDgpKIPlMxZ+tmSfibzQzeXbtm0bjwdrlQ/W3bi+4447xoI9tP596GaP+y644AJ/3XXX+WDtjZU98Zx44om5R6ZyvvDCC3PbhVbuvPNO/7e//c2HbrYoHqlIrIInzkMOOSSK3ZdeeskHK2u8d7BAxOh23XXXuP3RRx/ViT5YS2IelEPwUGkivtOByjF03/nQnRcPIUYQ0DS0QpedD9aymFYqzWIDlTOimmuDxcZTmVV7GDZsWHzO9Pt/0003xe+F/C01IDBomBgHa2yk4zv55JP90ksv7RExzR1odNCQo0GRDpQHwRqb3l2T2+QVjdzll1/e33vvvT70WkRxyTd9+eWX+9DN7vv16xffFfKTdz1YPv1TTz3lqR+CRTP+goXTP/roo/Hbp1ylDAgWylzjD7ihN8KHrnpPY6Rbt25+1KhRUZDyrXG/yy67LJcHNMYxGlDm9enTJ4pVzg9d6h4BzPk0vq1xyYXXX399PG+33XbznTp1ig1rztt7771j+XTAAQfkni3ZyESAL7roop7r0iFYy+P3QHyIait3EbcYNBRaloAEaMvyL+vdW4MARYyFbpi8nBAfzz77bN5jldiJlZAC8oQTTvBYLVnfdttt45J1CnqW/MwCyjpWCZY8W/DljEmlEmcfQjB038fCG4sW4aijjooWDdZDl2QUvF9++SWb/sYbb4yWr7hR4A8WESwRVNpUUlgmuNdJJ50UlwhM0jfbbLP50M0ZxTOFNRUNAdFqYpBCGytocBXwwaXAhy6xKJrz3RprGaKXCgcrZWNEEYzuueeefNH6Y4891g8aNMgjznmOdAjuBLGiMgGdPp7cJk1YclZffXUfXBw87xRxBpeH5GlVtw7TW2+9NW+6qPgLsct7QWJn8P+NggURgpWMfEfwv/baa4mz/m8VAUpDpLkD7xkNu3yB8gBrbDUGrImwxzqNVTKfta6p6UZE0gCjEbLnnnvG79bipDFLo/eYY46JopQeJALfMN8z5ZT1qNxwww3RmokI5HvmvUcw0qtCQ4z8J65ffvklxoHQw+LOeQhfyiDKn/POO8/T8KHB8Pbbb0frNNdi2UL4cY2F4L4RyxjSTzzGB6skPUXkOQ0MRCvHgytQvBRLLM+AsEVUImSt4UMPEPehcZoONGCIhx8Nd0S49eqwLylk09emt0k7P4XmIyAB2nwsqy6m1iBAKYywBuYLFKhYrloq0PVJwYcVAQFHYU3XFwUb4jIpOtlHpc6SVjg/ClGz7t52222xYsA6inDl+KWXXhrFHVYHuunp7kREIlARvVR255xzTrSgUmFQ6SEOg69kDglxcM90l+Xf//73WDFQ4HOc7rZNN900ViZs8yO9hOCb6tdff/1Y6VHJUDlgveC5OY+0pQMVGhUd6cUKs8kmm8RzTTinz09v446Qr1uP84488shonSH/H3roofSlcRuBVuh6u4BKj/TvsccetisuYcp+uu0qGRDMNDpwJaCrEYsO7h35AkK5kHsNLgpYvxobEO489/3335+7FHHBPqxj+QLvjH2DCADOw02EBkIh0ZovnvQ+hAFWuXwBQcS711BAmPCe04VLXMGHMNfd3NC1jT3O9xUGHsZvc5111okMcJGBXamNgXxpGD9+fCxb+CbpMbHyItmNbm4R9AyYwCN9dJdTlnINvTUWzM1p3XXXjeUODVELcON7sEB5F3ywffBBj2KRxisNXBOMsEb8ImKxzlOO0agZO3asx4KJ4CD+4D8cj9EzgwCGE71BiEveqYcffjgndumVCb7oHq4sKWNpGL311lsxWTDh2nSgR4R4KYcpT5KBHhbqRyynsHn11VejBRdBbS5Qdn4YqBXdDBDViGmszTyPQtMJSIA2nWHVxtAaBCitXkRVOpj1cdKkSblD7MMigPChoERUUVCWI9ASpnDjA8IiQ4U4ZMiQuI/99jMrqPl32n7r5mYbscES6wWClHXruseCSDc7+4iLrn4qFsQr+/hhhVpzzTVjBYuFgH1YNwhUCCOCu0I6UAHZ9cTLu4LgfP3113PWXNJIJc55FP52Pl3+FNJYO6hg2J+0GJoIT4teKiasjWa5SKcpuX3llVfGtCf3sW6iEbFEmsOsAOlT4vb+++8fLbR5D/53J/mFa0O+gCU66W+b75zm3IeogiNWWIQMvzBQLe4zEZG8HxV/vu8C6xjvk4nC5DX1rSMeEShY1dIBayONnnTgGtKMtZJ1hAPdwQg9rNQcQ2iUEmio0NjJ967Q60G3cqHANQgK7k95gLXtmWeeic/APvNvLnR9Y/cjhOBD3IgrvhuEDIG8Yz9d400NNEaIK+k7idBDfGHB5x3iW8Y6SUBQIp4sIJzgQNlCDwcB8UfXOfFQbiLW6JUxcWcWU86l94X7W9lhz8R9w8weUZwh0izveQ+I13oVEKrkBfew8o9GPI1nemgQknTtI0IpL4kLAWxlqFkz8e3E75Q8JiCg8V9Nfyf09lBuIbjJc9ylKDco0/At5b3G6opPKkKUstPKWutd4DyeGf9jAmwRzewLgzLjPv0pnYAEaOnsqv7K1iBA8RviYzdBBXQrCCkwLFDpcB6DI2jlIxCtq6Ux/oAWX0NLrExUghRiFLJ0XSVFIWmp7xdGyNc5boUs15jjPoUs1k9EocVlDvxhNHrcR+HK+cmABYTzsbwgVinYk8GEslk/4UU3q92DioN14maJELBjiEsLZilEuDLIwEJ9rhEIPqs47Px8SypGs6pYIwPLLulgoAEBiwp+iOlAJUHaG2p80CWHNSdfoJJhEEclAunFmouVmYAvKnlK3tGwobI2q5alB6sQLIwlQgO/NmsQ0PAoxgXB4uMdMGu87bOlVbgff/yx7YpL7s97Q0AU8A0kgzUWcJUoJWDhQxRbgNPTTz8dnxuxxPeB2EEAWUA4IEYQgmahTwqFLbbYwg8YMCAn0uy6UpdYyeGA0La0It7YZ98KYhS3iKYG3nUa5MkAgzA7SBR5dFPT+KDrG4soabB3wPy9adiQTvMLpcyCFedajw2igG0GBdGgQQjSmGQfx+jdQUwSzHrKN0/+YyFMlmVY87mOsgZLKOu826STdXoz+AZxByLQoGQ/v2Q8NHrtfDue7A6348keFivneC6+L9IAL67n26YsIs0cpwFlYWywbnIOPTcIU0RyOlD+MvARQUvvFT61yfSkz9d2fgISoPm5tIq9rUGAkhHWCsXSh1UTQZS2rDBYCcGSDlTUdBcXWzjQisaPk0qMSg5Lkw3GSMYdpizJDR5CCFshjmiwArIxSyy2Jh64zrqPEFJUmljjsMpxDAvFRhttFLvLOZ5PYFMZYT3AAoAlwILFyzNR8FohT3c19yHeZLq5FxxMjHIM4YfQx3pABQx78sQClQuCJl+g8idtxQQEBxZBKkwqCtJCl7AFey+S3c1UJJxLGhoKdMNhdc4XqNCSDZx85zTXPipxKm6EFGIFxliXCTAwIYWlimdlxDvizsSYjUjn+0AQ0kCj65l4ih1hisCFb75AGuw9wd2Cbw9rGo0OhIlZyREr6YB/aL7vMn1evm0aIbxjfBcITfu2+Faw4PF8iAiWDJrCIsf6iGDxRzRxPcKAfTAjwA3WMG8oMPIeyyJxIZoYDJO26uOrTINo6NCh0dpqcSJiEDyIOBpOWP6bGsjnpHsE8dnMALzHZ511VhyQyPPyY1Q8gfxjm3KN7wNrLRZyviv203BhaXnMuv1wnaEsREhSFlpek/cwv+uuu2LPCWUE11AOci4D1+Bm8WDZ5LuksU46abQiyhHA+MribkJPEkKZd58yxsocLLZchy+8CX7imjx5cnw++8OgKZ6Bd5PymffEymWEjolM3lPSZUaAfHUD7wti1dyQ7B62ROBTflLOMDgLMUqc6ca+na9lfgISoPm5tIq9rUWAkhkUGviU0YVEIZgOVLxMvZEvIMKorBsKWNooRBgwQwXCdCBWSFPImCWOeKjsOdd8G1lP/0w4JPdToCW3reC2fQzqYR0Lg3XH2zGsFgwSsi5EKiMq96QAs2c0QYEVlOvNn5HBAnQzcR2FNdY1q4BMAFPYWxcZVjlGvyNMqUSIC0FPPDaoiGNU6hbgXUhkwsTElZ3f0JIKlALfKpDk+eQTaaLrF3ELTypqxAfXwCFfNy5xUOGRHwjZZIAvcVoXY/JYOdYRRVSoNg0NVqpkQISRJ4gum62A9CE0aVDAHssY3edJayAVO3lRTIAt1p58XfdYqHDxgBOuETQC+J4sP+iloLLOFzgHAVFqIO+xnPHe8H5Splmw7lKEFQPr+FaZFoxuY8SqBfwJ+XaIi8D7a9+QnZNe8v7CmO5hyh7eIzgjOsgvC+QJXbsI3TRrmNHljJ8y30hTA8+b7Amy+LDQkVbYUH5YzwbWRcoRnp1yCgFHPpmQ5xryk6V9+/iRs81z9DpZkAAAQABJREFUsuSHGEz689ogSgSiCTzeT87lPWbgEPfBKm4ikmN2DsIUX07eVfIVcc55vH8s8R8nWNxci88njQEauvgXwzbtfkOji2fH9YAf13EeS374o1P28L4gTtmXz3eUe/Nt8Z5Q5qUDjRDSCTO+WQtmhdXoeiPS8FICtGFGmT2jNQnQhjKBgiTtPG7XUPhSCdUXKFSwAFH4sU5hSQFKBYIfJhYQCiybiohCGAGXLGCtoCt2aYUY5+eLh3tQsSIuzB+QwtsqfioFtukqImCNwJKALx7nYwkgMI0T98BtAJFGyx/LEaPYEbQcY8m9EL1muUgWviZkuRdppZuRdNggJwp8C5xDnEm/UI6RR8n02/lNXSII6ILGYoVIomLDWsO9uCeVKVaXfAGBQVp5DrqzsQSznRx4ke+65twHRypY0pAUWNyD94/0MOiMfLPAYCWEKXmLwKPS533B7cSEFiKcCj6fZdLiSS4Rn9zL/N04duN/LbIIsEKBhhlCzN7L5HkIZL4r0kK8pBv/XLpH852fvNbW6WKmQcQ7mrZA4i9I9y3Cx/jQ4CA9yTQjHhGdvLdwS8dj92JpvpZYNRE58ON8RAlduckBPFjwaNxhRcPyl7TQYxWz/GOKoaYEWBEHDe10gwrBQ77ZIEQGcNEowMKPvygCnoYW3wRpp7GWFJhci1WUHhPWrVHNOcnvmvRb1z5lAGxo/OKGxHVWhuGbTNmUtKjSbU+6cJHgXL4zcyPCuk3ZxX6b2YJyiviwLsOfrm4G3VnDAe5WFsPGGu48r/mvYqElToQk3wjrPBMNFd4VGtcI1nwBqzWCGHFMuZIMNHJ43zBC2Ldmx6k/EPoKxRGQAC2OUybPqiUBiuBKFv6WYVQaFDzmC2X700usGBTSFCi0+JMtY7rrqMgRJRRKbJvlgLj5WeFr2+kllbDts8KQbbO0sE4XuJ1DAcc63Uz44GENoPJJdpOasDQ/KgQXgsGEBNebtZElhTtWXSpyCmEKeSpUuydLCmhLH5aSpEXN/E+pWLgu6RuK5THZ8jf/W+6H+wCuE4zcTVeelg9UItyLyh+xUihQmWLBIC6EOQIyGRAKPB8WGLNUwZBns2mtkuezjlWdUfNYgomzOUctp+9VaNuEb7KrFn820k0hjcih0rUAR7NIk2dcZ/nId28Ci/eoWAFK3KNHj46Cgm+BRhhixSxNVOiIMiyiCAm+LQJ5x7uBAE4H0sTgM+LBT5BGD+8GgpJ3udD7kIyH++Hmkfwm7ThdpQxO4V2075djvHPcmy5d0sc7QYMIMYIlrL7A6HXecTgi7IkHAUv8xIOAtYCoMzcOs8ZTFiGEuI500aPSlIA7Cz69pMHKBbqvEfJmBebbxqoII8oonoHvifKMcoXvkOcmTZQ5NHaszOI7NyGF9Z3GDsfYjwUxGbgPYvK0006LcZGfCFCLC4HLfWHHvSizWPKzniu+WRrH7OM8Cwg7Gs+IYd5pszjT+KessgYtjQ7KQ8oJ8hbrMtZInpnnJF7KatJKuniPMU5YwxhfWr4JLKZ8Qwj3dCAOrMc0flinwWRhrzAQlH3pnhOOYwnHb1+hOAISoMVxyuRZfNyFpmrJ5APVk2hz/E92R1sLmNZxvkChi+WCH4W8iTsqGCw3yUDFw3kU7nTd0nq3QpfCqDE/CtfktdwPX7lkYU18FLx0q+Lkjq8R+6iA8PmkkmDbLCyscy5ddDwHlgOuQ7RYVznPQwXDufywINBdTdeRdY+xDgtEKN306W5orMJ2PYU311Fx4J/IfnxuLZAn+GVRWWFFKCQ2aBwQL4zxN+MHXwvkI12+NiE6liAsLLgBcE8aA1aBIm4QGelARYXFYkTwD6zWYCIUsUYlhnhBAPGcCGPcQAg8i3VPYnVDKCCi7XqsOzb9FVa4xgZY8s6YwOR6vgcaGaSDd8+sZdaww6eYvIAxDSbSjJWdMgiRhphIB0ZO8741FHAlQFzwXMlGDtchhGkMYbnH748BXBYQhnxndN3S4MLaxX8qqy8gaHkOegMIPC/b/HiHGYHPe0p3OwIIKyvHEIQ0eLAGM3iKfVgSrXeivnvWd8y4IuJpVCDi7FuFadKFByGF0MKP3dxqyB/OoxFBmkzE0eC2bne+dd4dAnnPebxTdFMjCJMBIWjPhG85PS5YSbmGn/GCO+UD1lD2W3lH45H8YpsyjQGW6WB+rZSTNPwtbpbcj6WVaTwn+WFlN3FxzK6hQU48lCEEGqO8B8bDenZogFI2II4RtEmXDVy7iB+rOu8R71ohlwrefZ5RoTgCEqDFccrkWbUkQMkgKkkKHioqClAsgjZaM52BVNhYyTiHQo4WOdfZ5MlYTixQMNFFSsWPJQJRj18X13I/CnIrzK3gSy8pBNmX7Jayc7BkYL0wUcF+CjuWWA4ZsYmfF+KSygKLD5Wr+QqyzbQlWKCY05LWP5PAI1SpBClkEZsIQQQChTZdRQgcLFGIWioarB1mUeS+3D85ytx8ZNlPdz4MEeV0j3FvKizSx2CNpOXUOOZbcj3xMeDHBoYQH/uwpnB/LE/kFfv4cRwhQF4YM6xrxIVFCGsFlguscghwLCqIGOLKZ+3Il66W2kce84wIJSpN8hmxwz7rfqTRQaVLvuFagDDiOJZvrG28C2mXkULPw79YJa+xYtElmW9eVUQM8XNuMtAgQGTYXKV0zyIosQCavyKNDr6vfAP5yC++Ia6rL9Adyrtq/zjBxATXkC7eORosfLtsY9nCN4970hBjH42UQi46dm8EJWINnnx/Nmob4cc63w7WLyxwiFpzVeA5+M7IE95HhLcNerK4G7uECeUNg74Qt1jve/23EcqShiHfBYLKRJrNiGD3oizgWtKFNZBvNhnMkocYRLxTxth7Y1OpIfyx6vGMBMQ1Ddt0MEuslXPkq4lO+MOTcorGCO8sjGBoMxRwD/LN4uEaGhMwwALOoCfKWPhaF7u9l4hy7mflB2nD6os4x9JpZTMNHr4ZSyO9BohVGPKe8B3Q8Ej/ByjiQ5jznvMbG/zOaQRYOjhOIK2ku6nuFv8/ttr4KwHaivO51gQo1hAKIgofWqEUBlgs0126NjefFaQULjbVDNcgRm20qVVqiC0bJEKBlxRjiB+uK+ZHoWgVhp1PAWmWDBNUdJHh18g51iVMZYDFMh0QjlgeqSixQNBax1pmIpJKjHvSVU8lSkVAvPhk0WWH8KaCYB33A4JNecR5WLsQJ+a4n7RmGUsqOAQJlY5VPOSBdQWn08w2lQ6VKRUkaeWZLVC5GCcqCOLGCoRwNm6IEiozhAmWQPbz/DQ6WEeIkk+4H7BNpdGQBczu35JLKmUEAe8C4ooKM/nfhhAKWEh5JqsEEaVYfslLGjnkAXzrC+QZ8XMelkWzPJmwsmsZ9IGFyAJdjzQYsIbRHVvfbAHwR9QUCryrNkAueQ7vDY0M3okbgxWLfOZ5aajxbDw/fBAUyXkxcVvAwgYLfowEp8eimIBANUsa3wH3SzZgTbjwbmJhpqFlgTKEcgbBzXopgevpreFb5NnoCcBaRzp4DkQh7wUNM/ZhHafMgy/70i4mCHDeecpAyoDTQgM0GZhWi3jsx3fIN2e9K7wbfMMIRc6hIcx3Sp6nn9G69y0u8oV8Sja4raHMOTwL96HhSrnG90seU3Zb+YFBIRno3cKwYOU25ReNUIQf5RMNAwukm3tT5iE8bVAl9yZtLGHJspSR62Ypp1FGGUiDl7zimRSKJyABWjyrzJ1ZSwLU/B7NMZ3MopCkIE9WUOynEMN/Mh2o6EzIUDBhPaPgwtJgXVpYdyjwsD4ixDiPH4WyrScLXdtnS0QTBb1tJ5dW8CKi2Y+VA3FAAWrdz+ZHlUw7VgO6uhCXxE36LHA/4jLXBIQpz4l1lPtR4XIcUYFfJet0reFIzzo/XAQorBGgDMiCC/cw6xKMTJxSaWG9oSIxDlTYWO+SlRb3J26EN2Kfbny2rZvZutHoyiVQkXBvSxPPSsXBNhUK1h7OsUrF/BaNA5U753JOFgKssCJj1cESRNqxjtLQwGLONnmdtPog2nBBwLpEF2J9wRpc6QYCTBFuSdGGeDGrDlY07s17hVixdxnxgwjiu0oO/iF9vM80ItKBd5l3JPkMnENeYfEiL7EA805wT8QgLgXcm/cWS29y3sd0/I3dplsai669p3DkvpSjlAt8LzTMCLyXWBCbK3BP3mkagfZM1riDMwG+JogZVMl3hwCid8Ys/sn08I3yPZo/Nt+3BfONp2zkuWgEUy7Sxcwzs498IJAXNtDIGnecY/n83nvvxWuIA8FLuYjYtHzjXPIZAc85HCNYg57jPDPp5R8HsI2Yo4FjecH5uBjhpmS+/ghUusMJvF9chzGAHirEMnysLEXAwwnXHRppvKcIfRoR9K6kv4MYaQN/MBBQzhEvwpxeFoXGEZAAbRyvTJ1dSwKUbkprGScziUKUQtW63+hCYTtfoLCjQqUwpOuKAg2G+ORRILNNgYYgRaRhKbX9FKwc52eWEgpAtk3YsY7gSvt6st/iMYsHXaFUSGYFRADnc3rnOZhwnTj4JcUnVgH2cS2WIwL+WwharIaIc4QtBaddzxyPpJtKBIGAmMT6hjUKdjCiSxtxAAusK2NDl5RdT6FOgDfriAUqQSomCmqEDcc4H+syQsmseKQdaw5iBysgnM0lgEqHa6h4uSciyQLMqAhIL+k2ZtZlbdZqhDr5mcWAxQUrJNZunpX3FL9Znt0C/CwfkoMm7HhyiXjCapUvYIVL+mYyYf9pwXpmecA7Q+B+WLDtnjTOqPjZxjplARHJu5wUE6xzXtJqZefbTAy2zdJ6IhBa5QqIEN5rm0CefytKBcn3RYMMX0d7Bt7rfI3YUtNGoxAe6cA3xjuNqGIAmFmibeQ7jTfeAfIE8ZYMfOuIaNJsgyZprGEdpAwkX61HgVHouG+Y9dPKEQQaQp9A/tOFTnx8z3xn/GiQ0k1uYo+yjLRQ/tBQYJuyhuej7GFJVzfx8P7g/gNP0kbes47YZknj1QJlMuUn7kUEynYEp71D+OAyop5vnHvwo/FMWiiTjSX7uRfvOdxodFBGKFSegARo5ZlX7I61JEARLlgw8gUKOHy4CIgoE0n5zqWQoluLQoquQSug8QtD4FDY2z7OIS4Tj2wnfxS+FNS2jwKVH4LP/tuQiVUELH5sFMqM5KRbk3V8r6iAkgNC8qUbgcB9sDpgpaBFzsdNBcIxKlYL+FpxLpYIuvQp7NnGwZ/CHN8orG2FAt3YnI+vKV1gnGvddFQwVNzW7Y2gRSgRqLC5jq50KjIC81patz/bDIaggiQOzrU8pYKm0sAqgpA1ywc+nZyHyMZvl4oOsQ0HuGHtIE/JMxO83CfrgQYBz40QZ4n4Qwzw/PiwNRRwVUj+g4Lk+Ywg52eBgSIm6pNzXdIw4d4wxgfSGjnmOoK1iYCYscFriBx+NOryjZpnwAffBO9+OmB15D7lDDSQeCYsvYgSBpuwzQ+LHT6FpB3OpVjNCqUdkcQ7ng5mkeSbNHcGRBPp4Tug58BEIxZCC8xmwDkjgv+6BQYX8g1hjYQj4pR4zeLKeYg7GhEEBB7n24AfrL80JAiwoBFEuWRilUGLWEvNPYRvk8anDVjjXpR95C3lIO8Bx7kPcWAJp7zDEkvaOd/+m5l95+w3f1TSkbR8UkbjPsJ1lEtW3iSNA/Ak7fZPPShn6b1K9pwRr0JlCEiAVoZzi9yllgQoljTmvssXsJjZMQo/Cm6zrCXPx9cSoYZPEl1bVDYUeMmRmggZunvZj4+WdUGyzc8EJQU3BR/bVgCyNOsrFodk5UalZv/bG6FkghExSaFvhXwyvcl1LIOIA6xaCEosI1gvqawQGxTIyUBXH5YLLBBYw4wP5/B8yVHoyetYx3IAByyhPDOVCFYEBkEgShGYxE+6EUqIYQtUEggWuu8JWDiIg0YCFREVqlkwEO+cTyA9WGKMM5UUooZKj+58KlOeGyFKY4S4EAg0OFgnICroxm8tAQEIE7pH+WEdK/TPGNLPjMDkPc8XeC+Mux0nz2GPmMA1BP9RtmmA4RaAVT8pThHBaYscjUCsbljfaWDkC1jtzMqXPk6XK89Z7sA7yTfD9827xLtMI5NueBozDMShbGjOgHhP+94SP41ExCLpoKyhVwDuvOcsTwuNS8QwDTOWDLzDUst5+XqEKEd4R5iKiQYCZUQyUI7YCG++Hb5VCzQs+DYtYF1E1Frgmyad+KbSKKWchRuNdtLKz6aF493lnYEtXfjJgCXTzoc5ZRSNSayfGAHSAUGKKwGNacQzLgc2gIwGNXFxLWliHV9TAmU0ZROCPM0hfQ9tl4eABGh5uFZFrLUkQBnhTGGVDmbtS04BRAVKQUSXlgWsB+zDMR/rHFZQuoZpTScDAzIoZDmfQtF8oyhok5ZQKnfiozJnaT9EFZUA1icKaPYjUq2LER/FEcFqQfc+Yo7jxXQPYaXAqpCu2K2rju7nZLA5PdPT2iDauGfSBzB5HetYLG3qI/MVpaKiUqbrFssWcSBq6SJLO+aTThPYxIflEt8+rGxUCnBl8AnduMRDhcE6IpeKln38SAOVD9ZOKmn24ZtrU7UkKxWrfMwVg/vWcsDqBa/0pNkmbEy0GyPEDI083n0G+VCxY91G1CNMEAlwt0B+IVYbG2iwYE3PF+o7lu/85tyHKEIAsyxHwEebLuZ0QNyTTwh3vhNEIeUS+2hY01vCADQC1kCEF1OWmftJOr6GtilH8OemDOIdQHCb/y73RPASsHwi3JLlKudbFzvn0tBA+LHOj256C6QP6z3xIULSFm96OHjXEPv8mObNLLEWR31LhCVlOP7TNPwpT0kDfrUYAazxzD4argotQ0ACtGW4V+SutSRAEU6IGAoUWveMWkfMsZ1PwFGI04LHgkMLnQ+BQpeArxOVKV1OFFTJQLc6Fj+6hhFSDL7AmmQFHPezH4LKRKlZQdnGx9L8QzmXAp/ClTixeHAu1gzWi7VokUYTbFg9EZBU2HQvYYnkPlj/EG2IBkQe+5KFLwISkZcWq8nnZx3/Qvy7LBhn4uP5iJv026ChdKVNxYJfrHWjEw+i3KZ+oaK3QIWBvxiVhqUZqwuCEisPwpf7EpdN6UJlxjXsR5hiXeZ4Y1ja/VvzEjEDI94PLKlYk7FipvMLBgw4gTfnHHjggRELVnwaG3xHxMP7ZwHBiitFYwP3Jj7e02Tg/eAeyfc1eTzr6zw3ZRENVhP/NK74TkzU8R7z3VHuIP7LFSg7YU05SO8IDXvEMWWhCUWOJxsclhaOY/2mhwKBynk00pPzs3IuPR0mPBkgxHl0z2O9pDeE9yw9OM3uUcyScoHyFEMB8VOmmrsKZRTriOtkGVRMvDqneQlIgDYvz6qKrZYEKOAp/BAxFJa0gLHuJLuI0pnD+bTuqYiTgXkGEVB051BAMRKdgDWPgpKuOSpsBoPgD0l3NIFBQohJ85fDskm3GR8Z1/FD7BJPsgXO+QhorAL48nFeqV18dHGRbqxV3Ac/K+JG1GG9xecJSwTbsOFepAW/LdKGaKSyry9g9SB+CnWzgDB62sQglQfPjoXXrCcWH6Ov4cfz2UAGuh9xEUB42Nymdn5ySX6ZdZN3G6sblSP5Y5V28nx8eBm0gh9ZsfOSJq+vhXXygQEudH1jseZdKRR4b+w9prHHe28NK7puLcCa82ywiO0vdsk7wPW8i4ghXGN4N/JZCIuNMwvnIbgYkU0vAI0Cyg4YkCcwZWYH3vV0eVWOZ6OLHDcErKzW8KObG1GKKMYK3lDge0X8IUaTwUbg2wwXHCOP8cvEso7vZqnln92HsodeJdwpKGcomzEo0GiinKIhQ4MKy7JCyxGQAG059mW/c60J0OYEihikEsQqwRJxhm8VlkqbngWhiv8SFQbdkPjJYR2l8LT/AELhh9XURClxULkgcPFrpIDHokEhT4ucLuaGBhw153PSTY2PKxaJ5GCEhu6BSEXgIjRxUyDtCEkc/HFjsGmxbPon4sPvFpYMkCAQB5PKM/CIbsPk4IJ4QoE/5AGVMZVKU6wkBaLX7noI4KeI6Me6xbtu048hHnD/wDLNu2z+zPVEVe8hGg+Mnud7wbcvbUGr9+IMH+Sb4P1GLOWbvL+lHo0GBiPM+eFfjrgsJtBApWxATDPwEj9Reln4P/XlDPjO824SbBoqnsGmkcLST1mUr+FaznQp7roEsiBA25Dk8LIoNJJAmLLFBZ88x1Kh8QSCuHHBouhC164L//PYBWHlQte5Cz5qcV8oVF2YHifuDy33eIMweMkFP0MXKmAX/LdcsBTF/aGgc8Ey6MK/wnOh294Fi44LBbkL3dExDo4Td7AoudBF1PjEttAVoZKMzxREtgvdWnVSAa/gRuDCwAEXhKoLYt4FC7UL1pM652kjWwSC+HB8G0GExvc1WNNd8NFzoUHggquEC5YzFwYSZeuhlNqyEeB9CQ1SFxq6jjIz+J+74E5QtvsRcbAau2AocEHIuyA8XXC/iXVh6JaP72jovXLBzSOmp6wJUeT1Egiuay64dDmW1RokQEvMGQnQEsEVuCy05qOQQkwFS4ULvpYuWO5csHi60Np2oWXvghUvisvgR+lC13admGhHha5OFwYNxHiCP5cLXVSZEpx1HqjIjWApdlRC8MmSuC7y8XSaCIhAFRKgvA2zNbjghuPCAK5oDAgWWBdcd6owtbWZJAnQVpzvEqCtOHP1aCIgAiIgAiKQYQJZEKBtM8xXSRcBERABERABERABEcggAQnQDGaakiwCIiACIiACIiACWSYgAZrl3FPaRUAEREAEREAERCCDBCRAM5hpSrIIiIAIiIAIiIAIZJmABGiWc09pFwEREAEREAEREIEMEpAAzWCmKckiIAIiIAIiIAIikGUCEqBZzj2lXQREQAREQAREQAQySEACNIOZpiSLgAiIgAiIgAiIQJYJSIBmOfeUdhEQAREQAREQARHIIAEJ0AxmmpIsAiIgAiIgAiIgAlkmIAGa5dxT2kVABERABERABEQggwQkQDOYaUqyCIiACIiACIiACGSZgARolnNPaRcBERABERABERCBDBKQAM1gpinJIiACIiACIiACIpBlAhKgWc49pV0EREAEREAEREAEMkhAAjSDmaYki4AItF4C06dPd+PHj3e///57631IPZkIiEDNE5AArflXQABEoGEC3377rWvTpo27+OKL8558yCGHuHnmmSfvsUrv/PPPP91VV13lEHLFhMceeyw+27hx44o5vdHnzDLLLO7qq69u8LoLL7zQrbPOOq5z586ud+/ermPHjm6//fZz33//fYPXNvaExjKqL/7ZZpvNXXbZZQVP+Z//+R/3l7/8Je/xZ599NrJ/6qmn8h7Pt3PgwIFu5ZVXzh365ptv3E033ZTb1ooIiEA2CEiAZiOflEoREIEiCQwbNswdfPDB7o8//ijyipY/7YQTTnAnnXSSW2655dw999zjXn31Vce+u+++2x155JHNnsAsMjIIiNmtttrKNt0BBxzgeB4FERCBbBFol63kKrUiIAIiUD8BrHtZCtdcc40755xz3KWXXuoOO+ywXNJXXXVVN9988zmsyzvvvLPbdNNNc8eaupI1Rsnn3WuvvZKbjmfBOq8gAiKQLQKygGYrv5RaEcgEgTPPPDNa9G677Ta34oorui5durj+/ftH30Z7gHXXXdfR/X3ggQe6+eef3y2//PLuzjvvdL/88ov729/+5rp37+622GILN2LECLskLu+77z639tprxy5/rttss83c+++/H4898MAD7uSTT47rdGcPHTo0rv/000/uiCOOcEsttZRbbLHF3Iknnui++OKLeMz+fPjhh26jjTaKXeBrrLGGe/DBB+1QXP7888/RstqnTx8377zzugEDBrjPPvuszjn4bu6zzz6uR48esZv4iSeeqHM838a///1vt/rqq7tDDz10psOwoWue+Cy88MILDnZzzz13fJbjjz/e/frrr3bYNcQ+HyPiXGuttRzpXXDBBd16663nvvvuu2hFxu1i6aWXdnPNNZdbbbXVHOktV3j55Zcji48//thtsskmMS94L5L3PPvss90ee+wRk3D44Ye7Z555xpH+VVZZxU2cONFNnTrV7b333pEZbiHrr7++e+WVV8qVZMUrAiJQIgEJ0BLB6TIREIHCBBBi+OX9/e9/j92lp5xyinvppZfcDjvskLvozTffjEIB0XD66ae7Dh06xO2NN97Y4dd31llnua+++srh82eB7ultttnG9evXz1177bXx2H/+8x+33XbbxVMQmFxPoGsW8YuFbNddd3X33nuvO+6442Ka7rrrrmhZjCf+98+2224bu8CxRiLoSOsnn3wSj3rv3YYbbui4brfddos+pvjFYqWcMmVKPIdrtt9+e/faa69FayYimvs2ZG18/fXXHYI3nxWvbdu27qijjnIrrLBCvMcbb7wRxWGnTp0iX6yj+JeSdgsNsc/H6Icffojd/vvvv3/01yR+xNs//vEPd+yxx0a+t956a0wH97r55pvtds26JB3wQ3zSADn//PPd7LPPHu8/duzYeC+e7913343rW265pVtkkUWizywCHpGMFRmfUt45fIEJCHbyS0EERKCKCISCVaEEAqHC8CNHjizhSl0iAtkjEAShD8WWv+iii/ImPvhc+mDlzB3bd999fRh844NVMbcviIkYRxBscV8QOT4IRP/bb7/F7SDE4vGVVlopd02wiMV9H3zwQdwXLF4+WFJzx1kJFrE68QahFLeD1TOeF6xncXvUqFG564JA8WGQjw+WU//oo4/G40EE544HURv3BYts3MeS53/ooYdy5wSLqJ9jjjl88N2M+/71r3/5du3a+WAVzZ1zyy23xOuCEMrtS66EgU/x+BVXXJHcXXA9CCkPnyBqc+cEi3CMg2ciFMM+zYjn4vmC9TQX74QJE3z79u0j39zOsBJEtQ8WWR8Ed9w966yz+uA+kDylzvoGG2zgg3W1zj7bCNbLeN8nn3wy7rK8CI0AOyXyJG3wJQSRHBnYCcES7YMV3DZ9r169fLBw57aDpdvvueeefsyYMbl9WhGB1k4g+JP70Miv6seUBTSUbAoiIALNT4CuXLq7LSy++OJxlS5SC3T7BtEWN5dddtm4pKvewgILLBBXP/roo7i85JJL3PDhw+M6Fq3nnnvO2TG67vOF0aNHRytZEG65w0EUuR9//NEtscQSuX2M1raAxXHOOed0NjKe+9DlTdd7ELLx995770UL64svvhgvw6JLN/BCCy1k0bgdd9zRYcUsFLD6EujebyiEmiRaB7HAJq2lW2+9tcNiaekgnmLY57sfXe8WeJ7QOMh1d9v+IObcpEmTctZh21/KkmfKF+g2twBPrKDkVzGBEfK4LWA5pnseP1ostssss0wxl+scERCBChEoXDJWKAG6jQiIQPUTMKFUaGojRGW3bt3qPEh6m+l6CMnR6T179sxdw3RFBHwsLZg4tW2mJGKEO8KU+OmORygRCokZhCK+oumQFHEcM7HLOseCZS83FydClO5hfDURmfZD9JlIxYeUbuNkCBbEKFqT+5LriCP8O82HNXnM1idPnhxXcUdAZCeZ2Tns+/zzz21zprzIxz53cmIlKZ7p8oZDkgunIm4JaR/auDPPH96dQu+NicquXbvWuRKhnwzkRfK9SR5Lr1933XXRL5ipoRCy8D3ttNMadIVIx6NtERCB8hKQAC0vX8UuAq2CQOhqjlYoRFC+gEUMX7xkSAu85DFbTwtM219oyeCTO+64wzHwBssmvqJHH310odPjfiyXiMd0QNglRWt96bW5ORnMhLUy+TN/RIRZvjk7G7JuMsgGq2oyLZZWBCf+royAZyAXaTSfUzuHJQOGkvzre5bkdel1awSwH1FImtLPZL6Uyful40luE0+h9wb/X0I6rlLTT1xwwlf466+/joPQ8K8944wzol8uxxVEQASqg4AEaHXkg1IhAlVPgAE3jKBOizmEHKOQ+/btW9ZnYJBP8BGMo9kZ0c6cmQiVt956K97X/nOQiRcb/LPkkks6RlWbcOJkuu2xVjIKv5hA9y2WTsQmYpwflj3SwWAoAt32WGPNqsc+zke01heYbB4xfd5558102uWXXx6FFCP9sWLCmG7lZOAeCDx4FBvSjPJdx8h3Qvp+bOOe0Lt373i8oT9Yi7GWPv300zOdysAwrJ24EJQaeBbLayytDPy6//774yCq3XffPc5mgMBlcJOCCIhA9RCQAK2evFBKRKCqCTA6HIsVUyMxvRFT29x+++1xdDhWJyZOL2egGxYx8/jjj0cfRKyDjMzGL5Qwbdq0uEQcERA3YSBNnBaJ/yq00047xZH4jGzHgkpcxc6tyYh6nhHByf0Rs4zcv+GGG+L0Rdxvl112iaOwGbXPcaaPsumCOF4oMHKeyeaZPuqYY46JQg2xhjDlHmuuuWYc0c/1TB/FSHxGd/P8dPsz5RD+s4X+21C++6YZ5TsHQQ0f0sT0SDNmzIgNkAsuuCBOnWUiNt+1yX3MBsBsBPjDhkFs0Vf1kUceceyHJfOfNiXwLLhZPP/889Fii1DnXWTWBcQ/YpTGA1N3KYiACFQPAQnQ6skLpUQEqpoAA4b4jzMMTGG6Hro2DzrooOgTiGXUfAPL+RDMAcn98XlEECLEmLuSrmOb65H5PxFPCJxzzz03Dh5C8OAmwDMsuuiiLoyIdmHkedFJZUoi7oPIZYogrHbME3rllVdGIUtEWFQRO4hCjjMdFAIu7UOZ76ZYPxFNYWaNeA0DohD5DPjhPubDyTMNHjw4CmiEF933uAeQtsZYEdOM8qWJfaQBoY64xeJLvpPnpKHYwNRIDBxDSA8aNCgKQf6TEaIRCy+T7DclwAi3hL/+9a9xKilEO1NNYTXm3gzaQkTTiFAQARGoHgJtgo9P/mGI1ZPGqkwJhSkjLVkqiECtEUAEMgk7A4bqG+VdLi6ISbrB6xNd+C4i0pJ+prgL0E2LWC7Wgpd+BsQO1sDk5PDpc+gSR/yQxsYGfEaxNNPFnUx7Mh66nJkPE3Fr4jR5vNj1fIzyXYvwhnlygFi+84rZR7oZQIZVurkC1Rj5QkPBAoOWeEfDtEwt8o5aOrQUgZYgQON0yJAhsZHaEvcv5p76V5zFUNI5IiACdQgwurvcPp91bpjaqE/82alYBtMhPbo6fbyYbSyvDYX0aPiGzk8eR5hhpa0vIPqbQwzmY5Tvvlg/m+N+xI0gbO5AYyIpPokfq3hzpbm506v4REAEnFMXvN4CERABERABERABERCBihKQAK0obt1MBERABERABERABERAAlTvgAiIgAiIgAiIgAiIQEUJSIBWFLduJgIiIAIiIAIiIAIiIAGqd0AEREAEREAEREAERKCiBCRAK4pbNxMBERABERABERABEZAA1TsgAiIgAiIgAiIgAiJQUQISoBXFrZuJgAiIgAiIgAiIgAhIgOodEAEREAEREAEREAERqCgBCdCK4tbNREAEREAEREAEREAEJED1DoiACIiACIiACIiACFSUgARoRXHrZiIgAiIgAiIgAiIgAhKgegdEQAREQAREQAREQAQqSkACtKK4dTMREAEREAEREAEREIF2QtD6Cfz+++/uueeec0OHDnUTJkxwnTt3dv3793f77rtv7uG//fZbN2zYMPfBBx+4zz//3E2bNs3NOeecbt1113Vt2rRxDz74oJs8ebLr0aOHW2edddyUKVPcK6+84maffXa31VZbuf3339/NMsssufhYGTt2rLvnnntifAsttJDbeeedXc+ePeM5n3zyiXv00Ufd1KlT3dJLL+0222wz1759+zrXa0MEREAEREAERKB1EmjjQ2idj1bep1pzzTXdhRde6FhWc7j//vvdQQcd5CZOnOjatWvnEKPLL7+8Gz16tOvdu7fr0qWL+/LLL93XX3/t2rZt6/7444+8j4MI7dixo/vpp59yx1dZZZUoVN95550oRK+66iq34IILuuuuu869+uqrbvz48W7RRRd1G2ywgfvuu++iwP3oo4/cu+++6/bYYw+3yy67uPnmm8/deOONUaT+8MMP8R7cYNy4cW7GjBmub9++brbZZsvdUysiIAIiIAIiIAL1E6CeHzJkSKzv6z+z5Y7KAtpy7Mt+Z0TggAED4n1mnXVWt9tuu7mnnnoqik92IvL4WSgkPjlOOyUpPhGkr7/+ul3qfvvtN7f33nvntm3lww8/dPy4Nx/DiiuuGOM588wz3fTp0+PvuOOOc6ecckoUw4hghHLXrl2jBZZrb7/9drfTTjtZlFqKgAiIgAiIgAhknIAEaMYzsFDy//zzT7f99ts7hOevv/7qjjnmGDdo0KBCpzd6P4IUsfjPf/7THX/88TNZThGoSeP6rbfe6p544okoPjt06OBOP/30aI2lO3/w4MG5+5PuOeaYw33xxRfur3/9q1tiiSVi1z3W0YEDB+bO04oIiIAIiIAIiEB2CWgQUnbzrt6Uv/fee9Hfc5555ol+nJMmTar3/OTBYru8EZgnnHBC8tLcelJ82k66+Qn4l1577bXRYvr888/HfXTFI5YJ+IUSxowZE31DWT/yyCOjtZR1BREQAREQAREQgWwTkADNdv4VTD0WRwYFIeroir/hhhsKnps+gO9lMQGRicWylHDggQdG30+uxcqJxRNLLWGBBRZwK6+8chzoxOAoBjMhWi+44IJ4XH9EQAREQAREQASyTUACNNv5VzD1DDpi5DnCrlhBWTCyMhygG37ttdeOMTPyftVVV43rjKpnwNKoUaNiF/5JJ53k+vTpE/1Chw8fXoaUKEoREAEREAEREIFKE5AArTTxCt2P6Y56h1HuDCwq1UpZzqR+//337oUXXsjd4s0334zrNjDJDjCKnsDo/ZEjRzquUxABERABERABEcg2AQnQbOdfwdRvt9127tlnn43Hzbey4MktcABLZzKk5xDlGPt23XXXeJodZ+qmfP6lybi0LgIiIAIiIAIiUN0EJECrO39KTh3THdlgomoUoFg6GUVvId8UUFhuzzjjjDhfKMd5JkR1NVp07Tm0FAEREAEREAERaJjA/ymAhs/VGRkjwNycyy23XG7+Tkaat2RgaiaCLZnYvr5glk7mASXgK0pXfD6xWl88OiYCIiACIiACIlBdBCRAqys/mjU1Cy+8sHvrrbdycdo0SLkdFV4xQbn55ptHEfrjjz8WnQKsuL/88stM/+6z6Ah0ogiIgAiIgAiIQNUQkACtmqxo/oTwP9rN2tj8sZcWI//l6KGHHsrrx2l+nhbzvPPOa6u5KZoWWWQRx78XVRABERABERABEcguAf0npOzmXYMp79WrV16h1+CFZTyBLnRC+j8lsS/ZtY5/6OTJk9mdC5deemkUoi+99JLbYYcdcvu1IgIiIAIiIAIikC0CsoBmK78alVp8JglJS2KjImjCycsss0z8l5qForDueLrWsYomw4ILLhjnBV1ttdUc/8nJwr777hv/G1I1DqqyNGopAiIgAiIgAiLQMIG6NX/D5+uMDBHAooi4w3ey0oH/3V7Mfe2/HyXT9/nnnzt+FrCGMvKd5zn55JPj/5S3Y1qKgAiIgAiIgAhkj4AsoNnLs6JTvMoqq0TRxpRHlQ5JAdmpU6dG3T7tt4r4xOpJPEzLtOGGGzYqPp0sAiIgAiIgAiJQXQQkQKsrP5o1NfwPdcScWUKbNfJGRDZ16tRGnO3y+q3yHGeddZY75ZRTGhWXThYBERABERABEag+AhKg1ZcnzZIiROe1117r3n777RifDf5plsgrFEnXrl1zd8L6edRRR+W2tSICIiACIiACIpBdAhKg2c27elOO/yWic+mll44T0aenOKr34mY82L59+1xsyf98ZDutu33OOed0yX/P2aVLl5huju+4446uR48emn7JoGkpAiIgAiIgAhknIAGa8QwslHwEHYLvm2++iUIOa2JaAM4999yFLq93v4nGfCetscYadSaL578xEWwgkV1j/ybURsP/9NNPcYS7He/QoYObMWOGI43Dhg1zm266qRszZowd1lIEREAEREAERCDDBCRAM5x59SUdkbjLLrvE6YywJvJfh9L/+vLnn3+uL4qZjmGFJJhonOmEsAOhSfd/OqT/f7sJ0/R5bDP10sSJE+Oh77//3g0dOtRNmzatzpRM+a7TPhEQAREQAREQgWwQkADNRj6VlEombmcaJqyNzKnJaPg55pgj7mNu0Mb6hU6aNKnBdIwcOTLvf18yF4D5558/xmGCNG2V5eB3330XRS5pHzhwoFt99dXd5Zdf7vr379/g/XWCCIiACIiACIhA9ROQAK3+PCo5hQg4JqO/+eabHf8XHguo+YbyX4aY2ghBSnf9iiuuGO/Tu3fv3P0amvB98cUXj+diYeXHtE8ELJidO3eO1tD55pvPbbHFFm7w4MEOAXvfffe5QYMG5SafR4iaCLVueeLo06dPFMgrr7yy4z533XVX9GflmIIIiIAIiIAIiEC2CWgi+mznX4OpR9ztueee8cfJd999t7v66qvdZ5995pgeCSsoQpP/sT5kyBDXr18/N2rUqGh5HD16dBSt+GL27NnTYb18+eWX3XLLLefoQh8/frwbMGCAO+CAA9xGG20UfT/pKsdXE2vrsssuG4VoMpHdu3eP1thjjz3WIXa//PLLOMk853AfBi2RZtwDuM/w4cPjxPOa+zNJUesiIAIiIAIikG0CEqDZzr9Gp3777bd3/OoLWB0RoYjMKVOmROumjWZHJDK1E36giFUGCyUD26uuumpyV951rLOIYCasf//996Ng7datWxTDCFMGTdU32ClvpNopAiIgAiIgAiKQCQISoJnIppZJJKKTLvRkoJvcutqT+0tZx9KJawA/BREQAREQAREQgdohIB/Q2slrPakIiIAIiIAIiIAIVAUBCdCqyAYlQgREQAREQAREQARqh4AEaO3ktZ5UBERABERABERABKqCgARoVWSDEiECIiACIiACIiACtUNAArR28lpPKgIiIAIiIAIiIAJVQUACtCqyQYkQAREQAREQAREQgdohIAFaO3mtJxUBERABERABERCBqiAgAVoV2aBEiIAIiIAIiIAIiEDtEJAArZ281pOKgAiIgAiIgAiIQFUQkACtimxQIkRABERABERABESgdghIgNZOXutJRUAEREAEREAERKAqCEiAVkU2KBEiIAIiIAIiIAIiUDsEJEBrJ6/1pCIgAiIgAiIgAiJQFQQkQKsiG5QIERABERABERABEagdAhKgtZPXelIREAEREAEREAERqAoCEqBVkQ1KhAiIgAiIgAiIgAjUDgEJ0NrJaz2pCIiACIiACIiACFQFAQnQqsgGJUIEREAEREAEREAEaoeABGjt5LWeVAREQAREQAREQASqgoAEaFVkgxIhAiIgAiIgAiIgArVDQAK0dvJaTyoCIiACIiACIiACVUFAArQqskGJEAEREAEREAEREIHaISABWjt5rScVAREQAREQAREQgaogIAFaFdmgRIiACIiACIiACIhA7RCQAK2dvNaTioAIiIAIiIAIiEBVEJAArYpsUCJEQAREQAREQAREoHYISIDWTl7rSUVABERABERABESgKghIgFZFNigRIiACIiACIiACIlA7BCRAayev9aQiIAIiIAIiIAIiUBUEJECrIhuUCBEQAREQAREQARGoHQISoLWT13pSERABERABERABEagKAu2qIhUZTMSMGTPciBEj3KhRo6oq9b///rsbOnSo69u3b1WlS4lpmMAXX3zh5plnHtehQ4eGT9YZVUNgypQp7o8//nDdunWrmjQpIQ0ToAz/6quv3MILL9zwyTqjqgiMGzfODRgwwHXq1Kmq0lVNifnuu++qKTl50yIBmhdLwzuvvPJKd9NNN7l27aoL4a+//ureeOMNx1IhWwQ+/fRT1717d9exY8dsJbzGUzt58uQoQHv06FHjJLL1+NOnT3cTJkxwP//8c7YSrtS6Dz74IBpZunbtKhoFCBxxxBGuX79+BY5Wx+42PoTqSIpS0RwEKFS7dOnipk2b1hzRKY4KElh//fXdaaed5tZbb70K3lW3aiqBwYMHO0Toeeed19SodH0FCbzzzjtup512cmPGjKngXXWr5iCA1frFF190Cy20UHNEpzhaiIB8QFsIvG4rAiIgAiIgAiIgArVKQAK0VnNezy0CIiACIiACIiACLURAArSFwOu2IiACIiACIiACIlCrBCRAazXn9dwiIAIiIAIiIAIi0EIEJEBbCLxuKwIiIAIiIAIiIAK1SkACtFZzXs8tAiIgAiIgAiIgAi1EQAK0hcDrtiIgAiIgAiIgAiJQqwQkQGs15/XcIiACIiACIiACItBCBDQRfQuBL9dt+b8CTLBc7f8BoVzPn+V49Z+Qspl7+k9I2cw3/hXn+PHj3eKLL57NB6jhVL///vvxPyG1b9++hilk/9ElQLOfh3oCERABERABERABEcgUAXXBZyq7lFgREAEREAEREAERyD4BCdDs56GeQAREQAREQAREQAQyRUACNFPZpcSKgAiIgAiIgAiIQPYJSIBmPw/1BCIgAiIgAiIgAiKQKQISoJnKLiVWBERABERABERABLJPQAI0+3moJxABERABERABERCBTBGQAM1UdimxIiACIiACIiACIpB9AhKg2c9DPYEIiIAIiIAIiIAIZIqABGimskuJFQEREAEREAEREIHsE5AAzX4e6glEQAREQAREQAREIFMEJEAzlV3/l1j+53spodTrSrmXrpmZQKn8//zzz5kj056KEig17yqaSN1sJgKl5lup182UAO0oiUCp/Eu9rqRE6qImEZAAbRK+yl/8xhtvuN1228116dLFLbLIIu7MM89sMBE//fSTO/74491iiy3m5plnHrftttu6b7/9tsHrdELzESgl395991232WabuU6dOrk55pjDrbLKKu6JJ55ovkQppgYJNPXb+fnnn92iiy7qDj300AbvpROal8DNN9/s1ltvvfjtrLbaam7EiBEN3mDSpEmxfOzatavr3bu3O/bYY90XX3zR4HU6ofkIlFJWkkfbbbedm2uuuVzHjh3dhhtu6Cg/FaqcQGgtKGSEQKjMfBCdftddd/XhI/U33XSTD8LEn3HGGfU+wWGHHeZDJeifeuop//zzz/sVVljBL7/88j5Y1eq9Tgebh0Ap+RYaCH6BBRbwQXT622+/3Qfh6bfcckvfrl07//rrrzdPwhRLgwSa+u0E4UlXhT/kkEMavJdOaD4Czz77rJ911ln9FVdc4d98801/8MEH+9lnnz2uF7rLtGnT/EILLeRXX311//TTT/s77rjDL7744j40+Atdov3NTKCUspJ6jHKyb9++ftiwYf7RRx/1q666ql9wwQX9Dz/80MwpVHTNScA1Z2SKq7wETj31VB+sYZ6C0gLis1u3bn769Om2q85y9OjRvm3btv6+++7L7X/vvfdipfjII4/k9mmlfARKybfrr78+5tGrr76aS9jUqVP9nHPO6RE1CuUn0NRvBxETLGmxISEBWv78St5hqaWWmkk49uvXz++zzz7J0+qsX3PNNT70LPkJEybk9j/88MNRlAbLaG6fVspHoJSy8uOPP45l5Q033JBL2HPPPRf3Pfjgg7l9Wqk+AuqCr3ILdTJ5jz/+uNt0001daMnndm+11Vbum2++ca+99lpuX3IlWD1dsAS4/v3753YvueSSLrTs3fDhw3P7tFI+AqXk24orruiuvPJKF1ryuYTRvRQaG+7777/P7dNK+Qg05duh6z6IHXfBBRdEt5c2bdqUL6GKuQ6Bzz//3IVGtttmm23q7N96663rLfOGDBnidtxxRxcsZ7nrcIH57LPPXPfu3XP7tFI+AqWUlZ07d3bByOKCtTOXsN9//z2u476kUL0EJECrN29mSllo6dUpHDmhZ8+e8Tx8l/KFjz76yM0777xRhCaPh+5d99VXXyV3ab1MBErJNwToQQcdVCdFI0eOdOPGjXNrrLFGnf3aKA+Bpnw7+A4uscQSbq+99ipP4hRrQQJ8bwQrG+1EyrzJkye7QgP6EJrLLrusu/vuu90WW2wRv7NBgwa5P/74w6LQsswESikr8dcNbmmxsXfjjTe6Bx54wB199NFu5ZVXdmuuuWaZU6zom0KgXVMu1rWVJRC6YKM1JXlXWn+Er7/+Ork7t/7jjz/OdA0HGcQkAZrDVNaVUvItnSAGs4Ru3DiQbL/99ksf1nYZCJT67Tz55JPu1ltvdW+//XYZUqUoGyLA90ZAmCQDZR5ikgGYNMqTIXROOhrx999/vwt+8tESOmXKFHfSSSe5999/32EdVSg/gVLLyuA+4YLvbux1IJXUi2PGjHHt27cvf6J1h5IJyAJaMrrKXxgGoLhZZpmlzo3p2uM3Y8aMOvttg/PpnkgHrvn111/Tu7VdBgKl5FsyGXTnhgFI7pNPPnFhYISbbbbZkoe1XiYCpXw7VKD77ruv++c//+l69epVppQp2voI8L0R0uWebecr94IPfSwPaTyEQUuOEfQvvviiO/nkk93QoUMdI7MVyk+glLIS48tKK60UxWYYhOTC2IY4YwiC9J133il/onWHkgnMrExKjkoXlptAjx49HK3yZMAfkNZ7IV+X+eeff6ZruJ7rCl2TjF/rTSdQSr7ZXfFrYkoRKsDHHnssFrR2TMvyEijl22G6M4QrFeLLL78cf7/88ku0rrFdqKFY3ieprdj53gjpstK28aVOhw4dOsQpfNZaa63oOmHH6dolFPKxt/O0bB4CpZSVYaBRtFLfdtttbocddojjJLBkU3aGwZzNkzDFUhYC6oIvC9byREqFmO42t+0+ffrkvSkfNIOU8HsyCwAnct3aa6+d9xrtbF4CpeQbKcCatvHGG7vx48fHOQzD9FnNmzDFVi+BUr6dMEWWGzt2rEPIJMOnn34afQvx4ZVlNEmm+ddNgFrZaHdgm3mQCzW88RFlbuVkYEAS5ab8QJNUyrdeSln5wgsvOAbW4nNtgXmTN9lkExdGw9suLauQgCygVZgphZK0/vrrRytY0ome7gYm3sXhOl/gGrpw8WuygKCha2Lddde1XVqWkUAp+YZVm4EQjOgl7yQ+y5hBBaIu5duhC5BvK/ljInosaexD5CiUlwAiBkFC2ZgMzPpRX5nHpPUM9LMR1Fwb5pSMjXe6cxXKT6CUspLBZgwgS/YuUEfS46Dvrfx51qQ7VN/MUEpRIQJBjMSJyI855hgfBkj4l156yYfpQfz555+fu4SJeIMPWp25QkOh64NA9cEy44O/jA9TMvkwOlAT0eeolXelmHwLFrKYb/yjAAJz2oUP2zMRehjQUucX/qNLPEd/yk+gmG/nuOOO8xdddFHBxDD/pOZuLYinLAcuv/xyHxrm/qGHHvLBBSLmDxPRf/jhh7n7pcvKDz74wAf3Cb///vvHcpLvjH/YwcT0v/32W+46rZSPQCllZZitwgcXCh/+E5IPQjTWc0cccUQsP5nHVaF6CWgi+urNm7wpC74tcXLrMIjIh+4kP3DgQB9a7LlzEacIl9B9m9sXLJ4+TN0T94dRgX6jjTbywRqTO66V8hNoKN9eeeWVmD/XXXddTExwj4jb5GX6F7qWyp9g3SESKObbWXjhhX19eSIBWvmXCcGICAmDWjxlJRPT85/jkiFfWcl/iyM/uYZrg0XOh6mbkpdpvcwEGltWkhz+Uxz/CYmykrzjH0DwzzwUqptAG5IXMk0hQwTIMnzJwr+Nczbis5jkMwceAyTwg1KoPIFS863yKdUd0wT07aSJZGOb0e2Mkg6islEJxvUFX9FC/qKNikwnN5pAqWXlxIkTY1c8ftZBiDb6vrqgsgQkQCvLW3cTAREQAREQAREQgZonoEFINf8KCIAIiABXzKcAAA18SURBVIAIiIAIiIAIVJaABGhleetuIiACIiACIiACIlDzBCRAa/4VEAAREAEREAEREAERqCwBCdDK8tbdREAEREAEREAERKDmCUiA1vwrIAAiIAIiIAIiIAIiUFkCEqCV5a27iYAIiIAIiIAIiEDNE5AArflXQABEQAREQAREQAREoLIEJEAry1t3EwEREAEREAEREIGaJyABWvOvgACIgAiIgAiIgAiIQGUJSIBWlrfuJgIiIAIiIAIiIAI1T0ACtOZfAQEQAREQAREQAREQgcoSkACtLG/dTQREQAREQAREQARqnoAEaM2/AgIgAiIgAiIgAiIgApUlIAFaWd66mwiIgAiIgAiIgAjUPAEJ0Jp/BQRABERABERABERABCpLQAK0srx1NxEQAREQAREQARGoeQISoDX/CgiACIiACIiACIiACFSWgARoZXnrbiIgAiIgAiIgAiJQ8wQkQGv+FRAAERABERABERABEagsAQnQyvLW3URABERABERABESg5glIgNb8KyAAIiACIiACIiACIlBZAhKgleWtu4mACIiACIiACIhAzROQAK35V0AAREAEREAEREAERKCyBCRAK8tbdxMBERABERABERCBmicgAVrzr4AAiIAItHYCU6dOdZ9//nlrf0w9nwiIQIYISIBmKLOUVBGoJQLrrLOOa9OmTe7Xtm1b17lzZ7fKKqu4a665plWheOKJJ+Jzfvrpp/G5/vzzT3fVVVe56dOnN+k577zzTtenTx8399xzu4UWWigut99+ezdlypQmxauLRUAERKCpBNo1NQJdLwIiIALlIrDUUku5U045JUbvvXc//PCDu/vuu90BBxwQ9w0cOLBct65ovD179nR77723m2uuueJ9hw0b5g4++GC35557lpyOO+64w+2yyy4xjsGDB7sFFljAPfXUU+7qq692q6++unv33Xddu3aqAkoGrAtFQASaRKBNKNR9k2LQxSIgAiJQBgJYQBFII0aMqBM71kGEKVa9V199tc6x1rJx2223ud1228399NNPrmPHjiU91lZbbeU+++wz98Ybb0TrqkVy4403un322cc9/fTTbv3117fdWoqACIhARQmoC76iuHUzERCBphKgK37ZZZd1H3/8cYzqhRdecGuttZajG3vBBRd06623nvvuu+/cH3/84S6++GK39NJLR8viaqut5v7973/Xuf26667r7rvvvij2unXr5hC9N910U51z2LjhhhvcSiutlIvnwQcfzJ3z8ssvR4si6dlkk02im8Dyyy8/072uvPJKt8QSS0RBucIKK7hLLrkkF8crr7wSXQu++OIL98ADD7iTTz45HiM9N998s9twww0dVsxkoHt+zTXXjOcn99s6z4+ATYfdd9/d3Xrrra5Xr165Q5x3xBFHRGG/2GKLuRNPPNGRFgtfffVVtNBiqZ133nnd1ltv7cxdgHMK5QHH6mPHcQUREIEaJYAFVEEEREAEqo3A2muv7YOYnClZP//8sw9d1T4IynjsoYce8rPMMosPgsrvuOOOfsstt4z7g4jzwYLqWd5///1+//33p7fHB4GZi7NTp04++JX6nXbayQdLqz/ttNNiXEFg5s45//zz4z7Oueeee/zhhx/ug2+qv/fee+M5jz76aNxeZJFF/B577OGvvfbamDbOCSItnkN83PuEE07wQfD6o446Km4Ha2Q8/vjjj8ftTz75xH/44Yc+uBjE7dBd7t9++22/7777+t69e8dz7Q9p4fm+/vpr21Vnecstt8Q44PSvf/3LT5gwoc5x2whCNTIL4t0Hseivu+46v+iii/ogMuMpv/76q+/Xr59feOGF/fXXX+9D175feeWVI7cvv/wynlMoDxpiZ2nQUgREoPYIuNp7ZD2xCIhAFgggQIP1MgoeRM/tt9/ug0XTB2tnFFahmzo+BuIHcXfmmWfmHgux1b59e3/22Wfn9rGy6667+h49enhEFQEBGqypPnTrx23+hO7pnNj7/vvvfejq93vttVfuOCs77LCDD5bCuA8Byv3POeec3Dmh6zvuQ/gRjjnmmNz5dtKxxx7rhwwZEjeTApQdwUIZrw+WyXg8dJfH7eByELf5Qxr69++f2863ghju0qVLvJY0BtcFf+qpp3pEvIVgFY7HR40aZbt88BX1oevfv//++/7yyy/3weoc1+2EMKred+3aNQpl9uXLg2LYWXxaioAI1B4BdcGHUllBBESgOgkwUGbnnXeOPwbU/O///q+bPHmyC8ItDrBJppqudwtvvvmm++2331ywSNquuGRQz6RJk1ywNOb2B1Fax0dym222cePGjYsjxYmHgU+rrrqqCwIt96Nb/6OPPnLffPNNLp6kPyUjzmeffXb3448/xuOM3Of8zTff3OHfySj08847b6b05SJLreAqgHsBg5MIQUC6IPocaa8v7Lfffi5YKd3DDz/sDjvssMjkjDPOcH/9618jB64dPXq0C9bb6GJgcW2wwQYx7bgM4GfL87NugcFSAwYMcCNHjrRdcZnOg2LZ1YlEGyIgAjVBQEMgayKb9ZAikE0C+Dia6ML3c7755is4chvRZ2Hs2LFRVDLyOxkQcQT8G5dccsm4bvviRvjTvXv3uIpgRIgSDj300LhM/7Hj7Mc3MhlmnXXW6IfKvtB9H+M699xz3fDhw+MzbLbZZnFE+vzzz5+8LO86z44Av+uuu6IvKH6iTFGFCGwoIIS5Fz/C0KFDHcL00ksvdYMGDXLvvfeey5cG4ifg65lmxH78QdNziybzwNjUxw6/WwUREIHaJCALaG3mu55aBDJBYLbZZoviBwGEmKxv2qDgB5p7ptA9jHuRC93AuX2sfPvtt3Ebi58Fs1LaNgOYCMHn0oXu67j+/PPPR6sjlsfkj4FJFkyw2XZ6efzxxzsG8zAVElMssWROzmIDg4cQda+//nqciopR7nPOOWfey7HwItYRu+mAVRhLJc9EYDYBLJXpgKUZhrDMN28onJIcuT6ZB41hl763tkVABFo/AQnQ1p/HekIRqDkCdJETnnnmmbi0P2wj2hCXFl566SVbjUuEWfATjQKO6Z4IWBznmGOO3A+r7IEHHhi7tOMJDfy56KKLovtA8Et1dG8zAh7LIN36TCuVDiZmk8eWW245FwYDxdH1jPivr/u9b9++jnsxYT+j4ZOBbSzExEXAEswIfhPn7MP6iyX4sccei7MI/Oc//3H8NyULCNNnn302zkZg+9LL5mKXjlfbIiACrYOABGjryEc9hQiIQIIA0xxtuummLgz+cUyTNGPGjCgiL7jggigcTeBxCRO2hwFMceomLIZ0TZ900kkxNqYkCoN94lRCTKOEJRDxF0bCR8ssFtpiAv6TiNAwMj0KOXxLw+ClOI0S3evpYJbNMNLehQFVucNYQS+77LIoLnm++sJxxx0Xn5mpoeh2f+2111wYOe822mij2HVuk9wzJyhzjeImgBjHeoq1Fr9V7sGz/v7773Hu0IkTJ0ZrKccRqUyeXyg0F7tC8Wu/CIhAxgnU3rgrPbEIiEAWCBSahimddhuBnZ5mKHQh+9DFHUdwB8HpQ5e0D8KpzuWMgg+DnOKo91CU++CT6MN/XqpzThCdPoi1OOUR5wR/yTil0y+//BLPs1HwwapY5zriDj6fuX1BEMbpjYiDUeWhG9wHX9R4PD0KnnsGER1Hpx9yyCG5OMaPHx+nfGKapmJCsNT6ZZZZJs4IwH2ZGYAplIIYrXM5o+s5j3P4McI/OeI+zPPpg6U0HiPtnBvEei6OQnnQELtcBFoRARGoOQL6T0ihtFUQARFovQSmTZsWR3zzP9HTAf9HJn0PUyLF/xqEr2k+iyTXhamb4uAluu+TFtR0nA1tM3AH/8hi/sMRPqxYQ833NQjQ6HeJmwCT7xcbSDvX2uj8Qtfh98kE93DI94wcJ6QHXBWKz/Y3FzuLT0sREIHsE5AAzX4e6glEQARKJJAUoCVGUZHLgrU1doMzlRLd90ydpCACIiACWSagaZiynHtKuwiIQE0Q4N9ZIj6ZUonBPwoiIAIikHUCsoBmPQeVfhEQgZIJvPPOO3G0e2O7lEu+YYkXMuH9iy++GCeET89tWmKUukwEREAEWpSABGiL4tfNRUAEREAEREAERKD2CMw8/0ftMdATi4AIiIAIiIAIiIAIVJCABGgFYetWIiACIiACIiACIiACzkmA6i0QAREQAREQAREQARGoKAEJ0Iri1s1EQAREQAREQAREQAQkQPUOiIAIiIAIiIAIiIAIVJSABGhFcetmIiACIiACIiACIiACEqB6B0RABERABERABERABCpKQAK0orh1MxEQAREQAREQAREQAQlQvQMiIAIiIAIiIAIiIAIVJSABWlHcupkIiIAIiIAIiIAIiIAEqN4BERABERABERABERCBihKQAK0obt1MBERABERABERABERAAlTvgAiIgAiIgAiIgAiIQEUJSIBWFLduJgIiIAIiIAIiIAIiIAGqd0AEREAEREAEREAERKCiBCRAK4pbNxMBERABERABERABEZAA1TsgAiIgAiIgAiIgAiJQUQISoBXFrZuJgAiIgAiIgAiIgAhIgOodEAEREAEREAEREAERqCiB/wdc87Z56hzUYgAAAABJRU5ErkJggg==" alt="Jitter plot of the propensity scores, which shows that no treated unit were dropped, and a large number of control units with low propensity scores were dropped." /></p>
<p>We can visually examine balance on the covariates using
<code>plot()</code> with <code>type = "density"</code>:</p>
-<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.out1, <span class="at">type =</span> <span class="st">"density"</span>, <span class="at">interactive =</span> <span class="cn">FALSE</span>,</span>
-<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a> <span class="at">which.xs =</span> <span class="sc">~</span>age <span class="sc">+</span> married <span class="sc">+</span> re75)</span></code></pre></div>
-<p><img 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" alt="Density plots of age, married and re75 in the unmatched and matched samples." /></p>
+<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a><span class="fu">plot</span>(m.out1, <span class="at">type =</span> <span class="st">"density"</span>, <span class="at">interactive =</span> <span class="cn">FALSE</span>,</span>
+<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a> <span class="at">which.xs =</span> <span class="sc">~</span>age <span class="sc">+</span> married <span class="sc">+</span> re75)</span></code></pre></div>
+<p><img 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" alt="Density plots of age, married and re75 in the unmatched and matched samples." /></p>
<p>Imbalances are represented by the differences between the black
(treated) and gray (control) distributions. Although
<code>married</code> and <code>re75</code> appear to have improved
@@ -722,14 +720,13 @@ balance after matching, the case is mixed for <code>age</code>.</p>
example, we can try a different matching method or make other changes to
the matching algorithm or distance specification. Below, we’ll try full
matching, which matches every treated unit to at least one control and
-every control to at least one treated unit <span class="citation">(<a href="#ref-hansen2004" role="doc-biblioref">Hansen 2004</a>; <a href="#ref-stuart2008a" role="doc-biblioref">Stuart and Green
-2008</a>)</span>. We’ll also try a different link (probit) for the
-propensity score model.</p>
-<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Full matching on a probit PS</span></span>
-<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>m.out2 <span class="ot"><-</span> <span class="fu">matchit</span>(treat <span class="sc">~</span> age <span class="sc">+</span> educ <span class="sc">+</span> race <span class="sc">+</span> married <span class="sc">+</span> </span>
-<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a> nodegree <span class="sc">+</span> re74 <span class="sc">+</span> re75, <span class="at">data =</span> lalonde,</span>
-<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a> <span class="at">method =</span> <span class="st">"full"</span>, <span class="at">distance =</span> <span class="st">"glm"</span>, <span class="at">link =</span> <span class="st">"probit"</span>)</span>
-<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a>m.out2</span></code></pre></div>
+every control to at least one treated unit <span class="citation">(<a href="#ref-hansen2004">Hansen 2004</a>; <a href="#ref-stuart2008a">Stuart and Green 2008</a>)</span>. We’ll also
+try a different link (probit) for the propensity score model.</p>
+<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a><span class="co"># Full matching on a probit PS</span></span>
+<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a>m.out2 <span class="ot"><-</span> <span class="fu">matchit</span>(treat <span class="sc">~</span> age <span class="sc">+</span> educ <span class="sc">+</span> race <span class="sc">+</span> married <span class="sc">+</span> </span>
+<span id="cb13-3"><a href="#cb13-3" tabindex="-1"></a> nodegree <span class="sc">+</span> re74 <span class="sc">+</span> re75, <span class="at">data =</span> lalonde,</span>
+<span id="cb13-4"><a href="#cb13-4" tabindex="-1"></a> <span class="at">method =</span> <span class="st">"full"</span>, <span class="at">distance =</span> <span class="st">"glm"</span>, <span class="at">link =</span> <span class="st">"probit"</span>)</span>
+<span id="cb13-5"><a href="#cb13-5" tabindex="-1"></a>m.out2</span></code></pre></div>
<pre><code>## A matchit object
## - method: Optimal full matching
## - distance: Propensity score
@@ -738,8 +735,8 @@ propensity score model.</p>
## - target estimand: ATT
## - covariates: age, educ, race, married, nodegree, re74, re75</code></pre>
<p>We can examine balance on this new matching specification.</p>
-<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Checking balance after full matching</span></span>
-<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.out2, <span class="at">un =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a><span class="co"># Checking balance after full matching</span></span>
+<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a><span class="fu">summary</span>(m.out2, <span class="at">un =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
<pre><code>##
## Call:
## matchit(formula = treat ~ age + educ + race + married + nodegree +
@@ -772,7 +769,7 @@ publishing the results of a matching analysis. This can be done either
in a table, using the values resulting from <code>summary()</code>, or
in a plot, such as a Love plot, which we can make by calling
<code>plot()</code> on the <code>summary()</code> output:</p>
-<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(<span class="fu">summary</span>(m.out2))</span></code></pre></div>
+<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" tabindex="-1"></a><span class="fu">plot</span>(<span class="fu">summary</span>(m.out2))</span></code></pre></div>
<p><img 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" alt="A love plot with matched dots below the threshold lines, indicaitng good balance after matching, in contrast to the unmatched dots far from the treshold lines, indicating poor balance before matching." /></p>
<p>Love plots are a simple and straightforward way to summarize balance
visually. See <code>vignette("assessing-balance")</code> for more
@@ -803,9 +800,9 @@ from the <code>matchit</code> object using <code>match.data()</code>.
This dataset only contains the matched units and adds columns for
<code>distance</code>, <code>weights</code>, and <code>subclass</code>
(described previously).</p>
-<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>m.data <span class="ot"><-</span> <span class="fu">match.data</span>(m.out2)</span>
-<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(m.data)</span></code></pre></div>
+<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a>m.data <span class="ot"><-</span> <span class="fu">match.data</span>(m.out2)</span>
+<span id="cb18-2"><a href="#cb18-2" tabindex="-1"></a></span>
+<span id="cb18-3"><a href="#cb18-3" tabindex="-1"></a><span class="fu">head</span>(m.data)</span></code></pre></div>
<pre><code>## treat age educ race married nodegree re74 re75 re78 distance weights subclass
## NSW1 1 37 11 black 1 1 0 0 9930.0460 0.6356769 1 52
## NSW2 1 22 9 hispan 0 1 0 0 3595.8940 0.2298151 1 61
@@ -822,22 +819,21 @@ in the estimation<a href="#fn4" class="footnote-ref" id="fnref4"><sup>4</sup></a
to estimate the ATT. We recommend using cluster-robust standard errors
for most analyses, with pair membership as the clustering variable;
<code>avg_comparisons()</code> makes this straightforward.</p>
-<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"marginaleffects"</span>)</span>
-<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>fit <span class="ot"><-</span> <span class="fu">lm</span>(re78 <span class="sc">~</span> treat <span class="sc">*</span> (age <span class="sc">+</span> educ <span class="sc">+</span> race <span class="sc">+</span> married <span class="sc">+</span> nodegree <span class="sc">+</span> </span>
-<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a> re74 <span class="sc">+</span> re75), <span class="at">data =</span> m.data, <span class="at">weights =</span> weights)</span>
-<span id="cb20-5"><a href="#cb20-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb20-6"><a href="#cb20-6" aria-hidden="true" tabindex="-1"></a><span class="fu">avg_comparisons</span>(fit,</span>
-<span id="cb20-7"><a href="#cb20-7" aria-hidden="true" tabindex="-1"></a> <span class="at">variables =</span> <span class="st">"treat"</span>,</span>
-<span id="cb20-8"><a href="#cb20-8" aria-hidden="true" tabindex="-1"></a> <span class="at">vcov =</span> <span class="sc">~</span>subclass,</span>
-<span id="cb20-9"><a href="#cb20-9" aria-hidden="true" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">subset</span>(m.data, treat <span class="sc">==</span> <span class="dv">1</span>),</span>
-<span id="cb20-10"><a href="#cb20-10" aria-hidden="true" tabindex="-1"></a> <span class="at">wts =</span> <span class="st">"weights"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"marginaleffects"</span>)</span>
+<span id="cb20-2"><a href="#cb20-2" tabindex="-1"></a></span>
+<span id="cb20-3"><a href="#cb20-3" tabindex="-1"></a>fit <span class="ot"><-</span> <span class="fu">lm</span>(re78 <span class="sc">~</span> treat <span class="sc">*</span> (age <span class="sc">+</span> educ <span class="sc">+</span> race <span class="sc">+</span> married <span class="sc">+</span> nodegree <span class="sc">+</span> </span>
+<span id="cb20-4"><a href="#cb20-4" tabindex="-1"></a> re74 <span class="sc">+</span> re75), <span class="at">data =</span> m.data, <span class="at">weights =</span> weights)</span>
+<span id="cb20-5"><a href="#cb20-5" tabindex="-1"></a></span>
+<span id="cb20-6"><a href="#cb20-6" tabindex="-1"></a><span class="fu">avg_comparisons</span>(fit,</span>
+<span id="cb20-7"><a href="#cb20-7" tabindex="-1"></a> <span class="at">variables =</span> <span class="st">"treat"</span>,</span>
+<span id="cb20-8"><a href="#cb20-8" tabindex="-1"></a> <span class="at">vcov =</span> <span class="sc">~</span>subclass,</span>
+<span id="cb20-9"><a href="#cb20-9" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">subset</span>(m.data, treat <span class="sc">==</span> <span class="dv">1</span>),</span>
+<span id="cb20-10"><a href="#cb20-10" tabindex="-1"></a> <span class="at">wts =</span> <span class="st">"weights"</span>)</span></code></pre></div>
<pre><code>##
## Term Contrast Estimate Std. Error z Pr(>|z|) 2.5 % 97.5 %
-## treat 1 - 0 1912 764.7 2.5 0.01241 413.2 3411
+## treat 1 - 0 1912 765 2.5 0.0124 413 3411
##
-## Prediction type: response
-## Columns: type, term, contrast, estimate, std.error, statistic, p.value, conf.low, conf.high</code></pre>
+## Columns: term, contrast, estimate, std.error, statistic, p.value, conf.low, conf.high</code></pre>
<p>The outcome model coefficients and tests should not be interpreted or
reported. See <code>vignette("estimating-effects")</code> for more
information on how to estimate effects and standard errors with
@@ -868,9 +864,9 @@ reported as summaries rather than in full detail), 5) the number of
matched, unmatched, and discarded units included in the effect
estimation, and 6) the method of estimating the treatment effect and
standard error or confidence interval (including the specific model used
-and the specific type of standard error). See <span class="citation">Thoemmes and Kim (<a href="#ref-thoemmes2011" role="doc-biblioref">2011</a>)</span> for a complete list of specific
-details to report. Below is an example of how we might write up the
-prior analysis:</p>
+and the specific type of standard error). See <span class="citation">Thoemmes and Kim (<a href="#ref-thoemmes2011">2011</a>)</span> for a complete list of
+specific details to report. Below is an example of how we might write up
+the prior analysis:</p>
<blockquote>
<p>We used propensity score matching to estimate the average marginal
effect of the treatment on 1978 earnings on those who received it
@@ -893,7 +889,7 @@ treatment, covariates, and their interaction as predictors and included
the full matching weights in the estimation. The <code>lm()</code>
function was used to fit the outcome, and the <code>comparisons()</code>
function in the <code>marginaleffects</code> package was used to perform
-g-comptuation in the matched sample to estimate the ATT. A
+g-computation in the matched sample to estimate the ATT. A
cluster-robust variance was used to estimate its standard error with
matching stratum membership as the clustering variable.</p>
<p>The estimated effect was $1912 (SE = 764.7, p = 0.012), indicating
diff --git a/inst/doc/assessing-balance.Rmd b/inst/doc/assessing-balance.Rmd
index 7a51463..a227673 100644
--- a/inst/doc/assessing-balance.Rmd
+++ b/inst/doc/assessing-balance.Rmd
@@ -52,7 +52,7 @@ Common recommendations for assessing balance include the following:
- **Variance Ratios**. The variance ratio is the ratio of the variance of a covariate in one group to that in the other. Variance ratios close to 1 indicate good balance because they imply the variances of the samples are similar [@austin2009].
-- **Empirical CDF Statistics**. Statistics related to the difference in the empirical cumulative density functions (eCDFs) of each covariate between groups allow assessment of imbalance across the entire covariate distribution of that covariate rather than just its mean or variance. The maximum eCDF difference, also known as the Kolmogorov-Smirnov statistic, is sometimes recommended as a useful supplement to SMDs for assessing balance [@austin2015] and is often used as a criterion to use in propensity score methods that attempt to optimize balance [e.g., @mccaffrey2004; @diamond2013]. Although the mean eCDF difference has not been as well studied, it provides a summary of imbalance that may be missed by relying solely on the maximum difference.
+- **Empirical CDF Statistics**. Statistics related to the difference in the empirical cumulative distribution functions (eCDFs) of each covariate between groups allow assessment of imbalance across the entire covariate distribution of that covariate rather than just its mean or variance. The maximum eCDF difference, also known as the Kolmogorov-Smirnov statistic, is sometimes recommended as a useful supplement to SMDs for assessing balance [@austin2015] and is often used as a criterion to use in propensity score methods that attempt to optimize balance [e.g., @mccaffrey2004; @diamond2013]. Although the mean eCDF difference has not been as well studied, it provides a summary of imbalance that may be missed by relying solely on the maximum difference.
- **Visual Diagnostics**. Visual diagnostics such as eCDF plots, empirical quantile-quantile (eQQ) plots, and kernel density plots can be used to see exactly how the covariate distributions differ from each other, i.e., where in the distribution the greatest imbalances are [@ho2007; @austin2009]. This can help to figure out how to tailor a matching method to target imbalance in a specific region of the covariate distribution.
diff --git a/inst/doc/assessing-balance.html b/inst/doc/assessing-balance.html
index 9c4fb7f..90be896 100644
--- a/inst/doc/assessing-balance.html
+++ b/inst/doc/assessing-balance.html
@@ -12,7 +12,7 @@
<meta name="author" content="Noah Greifer" />
-<meta name="date" content="2023-02-22" />
+<meta name="date" content="2023-06-13" />
<title>Assessing Balance</title>
@@ -362,7 +362,7 @@ code > span.er { color: #a61717; background-color: #e3d2d2; }
<h1 class="title toc-ignore">Assessing Balance</h1>
<h4 class="author">Noah Greifer</h4>
-<h4 class="date">2023-02-22</h4>
+<h4 class="date">2023-06-13</h4>
<div id="TOC">
@@ -422,8 +422,8 @@ assessed.</p>
covariate balance as part of a matching analysis. The tools available in
<code>MatchIt</code> for balance assessment should be used during the
process of selecting a good matching scheme and ensuring that the chosen
-scheme is adequate. These tools implement the recommendations of <span class="citation">Ho et al. (<a href="#ref-ho2007" role="doc-biblioref">2007</a>)</span> and others for assessing
-balance.</p>
+scheme is adequate. These tools implement the recommendations of <span class="citation">Ho et al. (<a href="#ref-ho2007">2007</a>)</span> and
+others for assessing balance.</p>
<p>In addition to the tools available in <code>MatchIt</code>, the
<code>cobalt</code> package has a suite of functions designed to assess
and display balance and is directly compatible with <code>MatchIt</code>
@@ -475,75 +475,73 @@ sample. In addition to computing SMDs on the covariates themselves, it
is important to compute them on squares, cubes, and higher exponents as
well as interactions between covariates. Several empirical studies have
examined the appropriateness for using SMDs in balance assessment,
-including <span class="citation">Belitser et al. (<a href="#ref-belitser2011" role="doc-biblioref">2011</a>)</span>, <span class="citation">Ali et al. (<a href="#ref-ali2014" role="doc-biblioref">2014</a>)</span>, and <span class="citation">Stuart, Lee, and Leacy (<a href="#ref-stuart2013" role="doc-biblioref">2013</a>)</span>; in general, there is often a high
-correlation between the mean or maximum absolute SMD and the degree of
-bias in the treatment effect.</p></li>
+including <span class="citation">Belitser et al. (<a href="#ref-belitser2011">2011</a>)</span>, <span class="citation">Ali et
+al. (<a href="#ref-ali2014">2014</a>)</span>, and <span class="citation">Stuart, Lee, and Leacy (<a href="#ref-stuart2013">2013</a>)</span>; in general, there is often a
+high correlation between the mean or maximum absolute SMD and the degree
+of bias in the treatment effect.</p></li>
<li><p><strong>Variance Ratios</strong>. The variance ratio is the ratio
of the variance of a covariate in one group to that in the other.
Variance ratios close to 1 indicate good balance because they imply the
-variances of the samples are similar <span class="citation">(<a href="#ref-austin2009" role="doc-biblioref">Austin
-2009</a>)</span>.</p></li>
+variances of the samples are similar <span class="citation">(<a href="#ref-austin2009">Austin 2009</a>)</span>.</p></li>
<li><p><strong>Empirical CDF Statistics</strong>. Statistics related to
-the difference in the empirical cumulative density functions (eCDFs) of
-each covariate between groups allow assessment of imbalance across the
-entire covariate distribution of that covariate rather than just its
-mean or variance. The maximum eCDF difference, also known as the
-Kolmogorov-Smirnov statistic, is sometimes recommended as a useful
-supplement to SMDs for assessing balance <span class="citation">(<a href="#ref-austin2015" role="doc-biblioref">Austin and Stuart
-2015</a>)</span> and is often used as a criterion to use in propensity
-score methods that attempt to optimize balance <span class="citation">(e.g., <a href="#ref-mccaffrey2004" role="doc-biblioref">McCaffrey, Ridgeway, and Morral 2004</a>; <a href="#ref-diamond2013" role="doc-biblioref">Diamond and Sekhon
-2013</a>)</span>. Although the mean eCDF difference has not been as well
-studied, it provides a summary of imbalance that may be missed by
-relying solely on the maximum difference.</p></li>
+the difference in the empirical cumulative distribution functions
+(eCDFs) of each covariate between groups allow assessment of imbalance
+across the entire covariate distribution of that covariate rather than
+just its mean or variance. The maximum eCDF difference, also known as
+the Kolmogorov-Smirnov statistic, is sometimes recommended as a useful
+supplement to SMDs for assessing balance <span class="citation">(<a href="#ref-austin2015">Austin and Stuart 2015</a>)</span> and is often
+used as a criterion to use in propensity score methods that attempt to
+optimize balance <span class="citation">(e.g., <a href="#ref-mccaffrey2004">McCaffrey, Ridgeway, and Morral 2004</a>; <a href="#ref-diamond2013">Diamond and Sekhon 2013</a>)</span>. Although
+the mean eCDF difference has not been as well studied, it provides a
+summary of imbalance that may be missed by relying solely on the maximum
+difference.</p></li>
<li><p><strong>Visual Diagnostics</strong>. Visual diagnostics such as
eCDF plots, empirical quantile-quantile (eQQ) plots, and kernel density
plots can be used to see exactly how the covariate distributions differ
from each other, i.e., where in the distribution the greatest imbalances
-are <span class="citation">(<a href="#ref-ho2007" role="doc-biblioref">Ho et al. 2007</a>; <a href="#ref-austin2009" role="doc-biblioref">Austin 2009</a>)</span>. This can help to figure
+are <span class="citation">(<a href="#ref-ho2007">Ho et al. 2007</a>; <a href="#ref-austin2009">Austin 2009</a>)</span>. This can help to figure
out how to tailor a matching method to target imbalance in a specific
region of the covariate distribution.</p></li>
<li><p><strong>Prognostic scores</strong>. The prognostic score is an
-estimate of the potential outcome under control for each unit <span class="citation">(<a href="#ref-hansen2008" role="doc-biblioref">Hansen
-2008</a>)</span>. Balance on the prognostic score has been shown to be
-highly correlated with bias in the effect estimate, making it a useful
-tool in balance assessment <span class="citation">(<a href="#ref-stuart2013" role="doc-biblioref">Stuart, Lee, and Leacy
-2013</a>)</span>. Estimating the prognostic score requires having access
-to the outcome data, and using it may be seen as violating the principle
-of separating the design and analysis stages of a matching analysis
-<span class="citation">(<a href="#ref-rubin2001" role="doc-biblioref">Rubin 2001</a>)</span>. However, because only the
+estimate of the potential outcome under control for each unit <span class="citation">(<a href="#ref-hansen2008">Hansen 2008</a>)</span>.
+Balance on the prognostic score has been shown to be highly correlated
+with bias in the effect estimate, making it a useful tool in balance
+assessment <span class="citation">(<a href="#ref-stuart2013">Stuart,
+Lee, and Leacy 2013</a>)</span>. Estimating the prognostic score
+requires having access to the outcome data, and using it may be seen as
+violating the principle of separating the design and analysis stages of
+a matching analysis <span class="citation">(<a href="#ref-rubin2001">Rubin 2001</a>)</span>. However, because only the
outcome values from the control group are required to use the prognostic
score, some separation is maintained.</p></li>
</ul>
<p>Several multivariate statistics exist that summarize balance across
the entire joint covariate distribution. These can be functions of the
above measures, like the mean or maximum absolute SMD or the generalized
-weighted distance [GWD; <span class="citation">Franklin et al. (<a href="#ref-franklin2014" role="doc-biblioref">2014</a>)</span>], which
-is the sum of SMDs for the covariates and their squares and
-interactions, or separate statistics that measure quantities that
-abstract away from the distribution of individual covariates, like the
-L1 distance <span class="citation">(<a href="#ref-iacus2011" role="doc-biblioref">Iacus, King, and Porro 2011</a>)</span>,
-cross-match test <span class="citation">(<a href="#ref-heller2010" role="doc-biblioref">Heller, Rosenbaum, and Small 2010</a>)</span>, or
-energy distance <span class="citation">(<a href="#ref-huling2020" role="doc-biblioref">Huling and Mak 2020</a>)</span>.</p>
+weighted distance [GWD; <span class="citation">Franklin et al. (<a href="#ref-franklin2014">2014</a>)</span>], which is the sum of SMDs for
+the covariates and their squares and interactions, or separate
+statistics that measure quantities that abstract away from the
+distribution of individual covariates, like the L1 distance <span class="citation">(<a href="#ref-iacus2011">Iacus, King, and Porro
+2011</a>)</span>, cross-match test <span class="citation">(<a href="#ref-heller2010">Heller, Rosenbaum, and Small 2010</a>)</span>, or
+energy distance <span class="citation">(<a href="#ref-huling2020">Huling
+and Mak 2020</a>)</span>.</p>
<p>Balance on the propensity score has often been considered a useful
measure of balance, but we do not necessarily recommend it except as a
supplement to balance on the covariates. Propensity score balance will
generally be good with any matching method regardless of the covariate
balancing potential of the propensity score, so a balanced propensity
-score does not imply balanced covariates <span class="citation">(<a href="#ref-austin2009" role="doc-biblioref">Austin 2009</a>)</span>.
-Similarly, it may happen that covariates may be well balanced even if
-the propensity score is not balanced, such as when covariates are
-prioritized above the propensity score in the matching specification
-(e.g., with genetic matching). Given these observations, the propensity
-score should not be relied upon for assessing covariate balance.
-Simulation studies by <span class="citation">Stuart, Lee, and Leacy (<a href="#ref-stuart2013" role="doc-biblioref">2013</a>)</span> provide
-evidence for this recommendation against relying on propensity score
-balance.</p>
+score does not imply balanced covariates <span class="citation">(<a href="#ref-austin2009">Austin 2009</a>)</span>. Similarly, it may happen
+that covariates may be well balanced even if the propensity score is not
+balanced, such as when covariates are prioritized above the propensity
+score in the matching specification (e.g., with genetic matching). Given
+these observations, the propensity score should not be relied upon for
+assessing covariate balance. Simulation studies by <span class="citation">Stuart, Lee, and Leacy (<a href="#ref-stuart2013">2013</a>)</span> provide evidence for this
+recommendation against relying on propensity score balance.</p>
<p>There has been some debate about the use of hypothesis tests, such as
t-tests or Kolmogorov-Smirnov tests, for assessing covariate balance.
The idea is that balance tests test the null hypothesis that the matched
sample has equivalent balance to a randomized experiment. There are
-several problems with balance tests, described by <span class="citation">Ho et al. (<a href="#ref-ho2007" role="doc-biblioref">2007</a>)</span> and <span class="citation">Imai,
-King, and Stuart (<a href="#ref-imai2008" role="doc-biblioref">2008</a>)</span>: 1) balance is a property of the
+several problems with balance tests, described by <span class="citation">Ho et al. (<a href="#ref-ho2007">2007</a>)</span> and
+<span class="citation">Imai, King, and Stuart (<a href="#ref-imai2008">2008</a>)</span>: 1) balance is a property of the
sample, not a of a population from which the sample was drawn; 2) the
power of balance tests depends on the sample size, which changes during
matching even if balance does not change; and 3) the use of hypothesis
@@ -602,14 +600,14 @@ will perform 1:1 nearest neighbor matching with replacement on the
propensity score, though the functionality is identical across all
matching methods except propensity score subclassification, which we
illustrate at the end.</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"MatchIt"</span>)</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(<span class="st">"lalonde"</span>, <span class="at">package =</span> <span class="st">"MatchIt"</span>)</span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="co">#1:1 NN matching w/ replacement on a logistic regression PS</span></span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>m.out <span class="ot"><-</span> <span class="fu">matchit</span>(treat <span class="sc">~</span> age <span class="sc">+</span> educ <span class="sc">+</span> race <span class="sc">+</span> married <span class="sc">+</span> </span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a> nodegree <span class="sc">+</span> re74 <span class="sc">+</span> re75, <span class="at">data =</span> lalonde,</span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a> <span class="at">replace =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>m.out</span></code></pre></div>
+<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"MatchIt"</span>)</span>
+<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a><span class="fu">data</span>(<span class="st">"lalonde"</span>, <span class="at">package =</span> <span class="st">"MatchIt"</span>)</span>
+<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a></span>
+<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a><span class="co">#1:1 NN matching w/ replacement on a logistic regression PS</span></span>
+<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a>m.out <span class="ot"><-</span> <span class="fu">matchit</span>(treat <span class="sc">~</span> age <span class="sc">+</span> educ <span class="sc">+</span> race <span class="sc">+</span> married <span class="sc">+</span> </span>
+<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a> nodegree <span class="sc">+</span> re74 <span class="sc">+</span> re75, <span class="at">data =</span> lalonde,</span>
+<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a> <span class="at">replace =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a>m.out</span></code></pre></div>
<pre><code>## A matchit object
## - method: 1:1 nearest neighbor matching with replacement
## - distance: Propensity score
@@ -679,8 +677,8 @@ functions of them in the matched sample. In particular, we request
balance on the square of <code>age</code>, the variables representing
whether <code>re74</code> and <code>re75</code> were equal to 0, and the
interaction between <code>educ</code> and <code>race</code>.</p>
-<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.out, <span class="at">addlvariables =</span> <span class="sc">~</span> <span class="fu">I</span>(age<span class="sc">^</span><span class="dv">2</span>) <span class="sc">+</span> <span class="fu">I</span>(re74<span class="sc">==</span><span class="dv">0</span>) <span class="sc">+</span> </span>
-<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">I</span>(re75<span class="sc">==</span><span class="dv">0</span>) <span class="sc">+</span> educ<span class="sc">:</span>race)</span></code></pre></div>
+<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="fu">summary</span>(m.out, <span class="at">addlvariables =</span> <span class="sc">~</span> <span class="fu">I</span>(age<span class="sc">^</span><span class="dv">2</span>) <span class="sc">+</span> <span class="fu">I</span>(re74<span class="sc">==</span><span class="dv">0</span>) <span class="sc">+</span> </span>
+<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a> <span class="fu">I</span>(re75<span class="sc">==</span><span class="dv">0</span>) <span class="sc">+</span> educ<span class="sc">:</span>race)</span></code></pre></div>
<pre><code>##
## Call:
## matchit(formula = treat ~ age + educ + race + married + nodegree +
@@ -808,9 +806,9 @@ bottom right corner of the plot, and any keyword value available to
supplied to <code>x</code> in <code>legend()</code> is allowed.</li>
</ul>
<p>Below we create a Love plot of the covariates.</p>
-<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>m.sum <span class="ot"><-</span> <span class="fu">summary</span>(m.out, <span class="at">addlvariables =</span> <span class="sc">~</span> <span class="fu">I</span>(age<span class="sc">^</span><span class="dv">2</span>) <span class="sc">+</span> <span class="fu">I</span>(re74<span class="sc">==</span><span class="dv">0</span>) <span class="sc">+</span> </span>
-<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">I</span>(re75<span class="sc">==</span><span class="dv">0</span>) <span class="sc">+</span> educ<span class="sc">:</span>race)</span>
-<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.sum, <span class="at">var.order =</span> <span class="st">"unmatched"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a>m.sum <span class="ot"><-</span> <span class="fu">summary</span>(m.out, <span class="at">addlvariables =</span> <span class="sc">~</span> <span class="fu">I</span>(age<span class="sc">^</span><span class="dv">2</span>) <span class="sc">+</span> <span class="fu">I</span>(re74<span class="sc">==</span><span class="dv">0</span>) <span class="sc">+</span> </span>
+<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a> <span class="fu">I</span>(re75<span class="sc">==</span><span class="dv">0</span>) <span class="sc">+</span> educ<span class="sc">:</span>race)</span>
+<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a><span class="fu">plot</span>(m.sum, <span class="at">var.order =</span> <span class="st">"unmatched"</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" alt="A love plot with most matched dots below the threshold lines, indicaitng good balance after matching, in contrast to the unmatched dots far from the treshold lines, indicating poor balance before matching." /></p>
<p>From this plot it is clear to see that balance was quite poor prior
to matching, but full matching improved balance on all covariates, and
@@ -855,9 +853,9 @@ names of the desired variables. If any variables are not in the
supplied with a data set containing the named variables.</li>
</ul>
<p>Below, we demonstrate the eQQ plot:</p>
-<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co">#eQQ plot</span></span>
-<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.out, <span class="at">type =</span> <span class="st">"qq"</span>, <span class="at">which.xs =</span> <span class="sc">~</span>age <span class="sc">+</span> nodegree <span class="sc">+</span> re74)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" alt="eQQ plots of age, nodegree, and re74 in the unmatched and matched samples." /></p>
+<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="co">#eQQ plot</span></span>
+<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="fu">plot</span>(m.out, <span class="at">type =</span> <span class="st">"qq"</span>, <span class="at">which.xs =</span> <span class="sc">~</span>age <span class="sc">+</span> nodegree <span class="sc">+</span> re74)</span></code></pre></div>
+<p><img src="data:image/png;base64,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" alt="eQQ plots of age, nodegree, and re74 in the unmatched and matched samples." /></p>
<p>The y-axis displays the each value of the covariate for the treated
units, and the x-axis displays the the value of the covariate at the
corresponding quantile in the control group. When values fall on the 45
@@ -869,8 +867,8 @@ to compute the eQQ difference statistics that are displayed in
<code>summary.matchit()</code> with
<code>standardize = FALSE</code>.</p>
<p>Below, we demonstrate the eCDF plot:</p>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co">#eCDF plot</span></span>
-<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.out, <span class="at">type =</span> <span class="st">"ecdf"</span>, <span class="at">which.xs =</span> <span class="sc">~</span>educ <span class="sc">+</span> married <span class="sc">+</span> re75)</span></code></pre></div>
+<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="co">#eCDF plot</span></span>
+<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="fu">plot</span>(m.out, <span class="at">type =</span> <span class="st">"ecdf"</span>, <span class="at">which.xs =</span> <span class="sc">~</span>educ <span class="sc">+</span> married <span class="sc">+</span> re75)</span></code></pre></div>
<p><img src="data:image/png;base64,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" alt="eCDF plots of educ, married, and re75 in the unmatched and matched samples." /></p>
<p>The x-axis displays the covariate values and the y-axis displays the
proportion of the sample at or less than that covariate value. Perfectly
@@ -883,8 +881,8 @@ difference between the eCDFs lines of each treatment group is used to
compute the eCDF difference statistics that are displayed in
<code>summary.matchit()</code> with <code>standardize = TRUE</code>.</p>
<p>Below, we demonstrate the density plot:</p>
-<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="co">#density plot</span></span>
-<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.out, <span class="at">type =</span> <span class="st">"density"</span>, <span class="at">which.xs =</span> <span class="sc">~</span>age <span class="sc">+</span> educ <span class="sc">+</span> race)</span></code></pre></div>
+<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="co">#density plot</span></span>
+<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a><span class="fu">plot</span>(m.out, <span class="at">type =</span> <span class="st">"density"</span>, <span class="at">which.xs =</span> <span class="sc">~</span>age <span class="sc">+</span> educ <span class="sc">+</span> race)</span></code></pre></div>
<p><img src="data:image/png;base64,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" alt="Density plots of age, educ, and race in the unmatched and matched samples." /></p>
<p>The x-axis displays the covariate values and the y-axis displays the
density of the sample at that covariate value. For categorical
@@ -907,11 +905,11 @@ each subclass as unbiased. The <code>plot.summary.matchit()</code> and
or for each subclass. We demonstrate this below. First we will perform
propensity score subclassification using 4 subclasses (typically more is
beneficial).</p>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Subclassification on a logistic regression PS</span></span>
-<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>s.out <span class="ot"><-</span> <span class="fu">matchit</span>(treat <span class="sc">~</span> age <span class="sc">+</span> educ <span class="sc">+</span> race <span class="sc">+</span> married <span class="sc">+</span> </span>
-<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a> nodegree <span class="sc">+</span> re74 <span class="sc">+</span> re75, <span class="at">data =</span> lalonde,</span>
-<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a> <span class="at">method =</span> <span class="st">"subclass"</span>, <span class="at">subclass =</span> <span class="dv">4</span>)</span>
-<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a>s.out</span></code></pre></div>
+<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="co">#Subclassification on a logistic regression PS</span></span>
+<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a>s.out <span class="ot"><-</span> <span class="fu">matchit</span>(treat <span class="sc">~</span> age <span class="sc">+</span> educ <span class="sc">+</span> race <span class="sc">+</span> married <span class="sc">+</span> </span>
+<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a> nodegree <span class="sc">+</span> re74 <span class="sc">+</span> re75, <span class="at">data =</span> lalonde,</span>
+<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a> <span class="at">method =</span> <span class="st">"subclass"</span>, <span class="at">subclass =</span> <span class="dv">4</span>)</span>
+<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a>s.out</span></code></pre></div>
<pre><code>## A matchit object
## - method: Subclassification (4 subclasses)
## - distance: Propensity score
@@ -922,7 +920,7 @@ beneficial).</p>
<p>When using <code>summary()</code>, the default is to display balance
only in aggregate using the subclassification weights. This balance
output looks similar to that for other matching methods.</p>
-<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(s.out)</span></code></pre></div>
+<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a><span class="fu">summary</span>(s.out)</span></code></pre></div>
<pre><code>##
## Call:
## matchit(formula = treat ~ age + educ + race + married + nodegree +
@@ -969,7 +967,7 @@ subclasses for which balance is to be displayed. Below we call
<code>summary()</code> and request balance to be displayed on all
subclasses (setting <code>un = FALSE</code> to suppress balance in the
original sample):</p>
-<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(s.out, <span class="at">subclass =</span> <span class="cn">TRUE</span>, <span class="at">un =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a><span class="fu">summary</span>(s.out, <span class="at">subclass =</span> <span class="cn">TRUE</span>, <span class="at">un =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
<pre><code>##
## Call:
## matchit(formula = treat ~ age + educ + race + married + nodegree +
@@ -1038,8 +1036,8 @@ original sample):</p>
also displays balance for the subclasses using
<code>plot.summary.matchit()</code> on a <code>summary.matchit()</code>
object with <code>subclass = TRUE</code>.</p>
-<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>s <span class="ot"><-</span> <span class="fu">summary</span>(s.out, <span class="at">subclass =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(s, <span class="at">var.order =</span> <span class="st">"unmatched"</span>, <span class="at">abs =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a>s <span class="ot"><-</span> <span class="fu">summary</span>(s.out, <span class="at">subclass =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a><span class="fu">plot</span>(s, <span class="at">var.order =</span> <span class="st">"unmatched"</span>, <span class="at">abs =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" alt="Love plot of balance before and after subclassification, with subclass IDs representing balance within each subclass in addition to dots representing balance overall." /></p>
<p>Note that for some variables, while the groups are balanced in
aggregate (black dots), the individual subclasses (gray numbers) may not
@@ -1051,9 +1049,9 @@ be displayed in aggregate or within subclasses again using the
<code>subclass</code> option, which functions the same as it does with
<code>summary.matchit()</code>. Below we demonstrate checking balance
within a subclass.</p>
-<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(s.out, <span class="at">type =</span> <span class="st">"density"</span>, <span class="at">which.xs =</span> <span class="sc">~</span>educ <span class="sc">+</span> married <span class="sc">+</span> re75,</span>
-<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a> <span class="at">subclass =</span> <span class="dv">1</span>)</span></code></pre></div>
-<p><img 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" alt="Density plots of educ, married, and re75 in the unmatched sample and in subclass 1." /></p>
+<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a><span class="fu">plot</span>(s.out, <span class="at">type =</span> <span class="st">"density"</span>, <span class="at">which.xs =</span> <span class="sc">~</span>educ <span class="sc">+</span> married <span class="sc">+</span> re75,</span>
+<span id="cb16-2"><a href="#cb16-2" tabindex="-1"></a> <span class="at">subclass =</span> <span class="dv">1</span>)</span></code></pre></div>
+<p><img 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" alt="Density plots of educ, married, and re75 in the unmatched sample and in subclass 1." /></p>
<p>If we had set <code>subclass = FALSE</code>, plots would have been
displayed in aggregate using the subclassification weights. If
<code>subclass</code> is unspecified, a prompt will ask us for which
@@ -1079,7 +1077,7 @@ multi-category, continuous, and time-varying treatments. The main
(<code>vignette("cobalt", package = "cobalt")</code>) contains many
examples of its use with <code>MatchIt</code> objects, so we only
provide a short demonstration of its capabilities here.</p>
-<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"cobalt"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"cobalt"</span>)</span></code></pre></div>
<div id="bal.tab" class="section level3">
<h3><code>bal.tab()</code></h3>
<p><code>bal.tab()</code> produces tables of balance statistics similar
@@ -1087,7 +1085,7 @@ to <code>summary.matchit()</code>. The columns displayed can be
customized to limit how much information is displayed and isolate
desired information. We call <code>bal.tab()</code> with a few of its
options specified below:</p>
-<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="fu">bal.tab</span>(m.out, <span class="at">un =</span> <span class="cn">TRUE</span>, <span class="at">stats =</span> <span class="fu">c</span>(<span class="st">"m"</span>, <span class="st">"v"</span>, <span class="st">"ks"</span>))</span></code></pre></div>
+<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a><span class="fu">bal.tab</span>(m.out, <span class="at">un =</span> <span class="cn">TRUE</span>, <span class="at">stats =</span> <span class="fu">c</span>(<span class="st">"m"</span>, <span class="st">"v"</span>, <span class="st">"ks"</span>))</span></code></pre></div>
<pre><code>## Balance Measures
## Type Diff.Un V.Ratio.Un KS.Un Diff.Adj V.Ratio.Adj KS.Adj
## distance Distance 1.794 0.921 0.644 0.004 0.992 0.049
@@ -1133,14 +1131,14 @@ several matching specifications. For example, if we wanted to compare
the full matching results to the results of nearest neighbor matching
without replacement, we could supply both to <code>bal.tab()</code>,
which we demonstrate below:</p>
-<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Nearest neighbor (NN) matching on the PS</span></span>
-<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>m.out2 <span class="ot"><-</span> <span class="fu">matchit</span>(treat <span class="sc">~</span> age <span class="sc">+</span> educ <span class="sc">+</span> race <span class="sc">+</span> married <span class="sc">+</span> </span>
-<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a> nodegree <span class="sc">+</span> re74 <span class="sc">+</span> re75, <span class="at">data =</span> lalonde)</span>
-<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb20-5"><a href="#cb20-5" aria-hidden="true" tabindex="-1"></a><span class="co">#Balance on covariates after full and NN matching</span></span>
-<span id="cb20-6"><a href="#cb20-6" aria-hidden="true" tabindex="-1"></a><span class="fu">bal.tab</span>(treat <span class="sc">~</span> age <span class="sc">+</span> educ <span class="sc">+</span> race <span class="sc">+</span> married <span class="sc">+</span> </span>
-<span id="cb20-7"><a href="#cb20-7" aria-hidden="true" tabindex="-1"></a> nodegree <span class="sc">+</span> re74 <span class="sc">+</span> re75, <span class="at">data =</span> lalonde, </span>
-<span id="cb20-8"><a href="#cb20-8" aria-hidden="true" tabindex="-1"></a> <span class="at">un =</span> <span class="cn">TRUE</span>, <span class="at">weights =</span> <span class="fu">list</span>(<span class="at">full =</span> m.out, <span class="at">nn =</span> m.out2))</span></code></pre></div>
+<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" tabindex="-1"></a><span class="co">#Nearest neighbor (NN) matching on the PS</span></span>
+<span id="cb20-2"><a href="#cb20-2" tabindex="-1"></a>m.out2 <span class="ot"><-</span> <span class="fu">matchit</span>(treat <span class="sc">~</span> age <span class="sc">+</span> educ <span class="sc">+</span> race <span class="sc">+</span> married <span class="sc">+</span> </span>
+<span id="cb20-3"><a href="#cb20-3" tabindex="-1"></a> nodegree <span class="sc">+</span> re74 <span class="sc">+</span> re75, <span class="at">data =</span> lalonde)</span>
+<span id="cb20-4"><a href="#cb20-4" tabindex="-1"></a></span>
+<span id="cb20-5"><a href="#cb20-5" tabindex="-1"></a><span class="co">#Balance on covariates after full and NN matching</span></span>
+<span id="cb20-6"><a href="#cb20-6" tabindex="-1"></a><span class="fu">bal.tab</span>(treat <span class="sc">~</span> age <span class="sc">+</span> educ <span class="sc">+</span> race <span class="sc">+</span> married <span class="sc">+</span> </span>
+<span id="cb20-7"><a href="#cb20-7" tabindex="-1"></a> nodegree <span class="sc">+</span> re74 <span class="sc">+</span> re75, <span class="at">data =</span> lalonde, </span>
+<span id="cb20-8"><a href="#cb20-8" tabindex="-1"></a> <span class="at">un =</span> <span class="cn">TRUE</span>, <span class="at">weights =</span> <span class="fu">list</span>(<span class="at">full =</span> m.out, <span class="at">nn =</span> m.out2))</span></code></pre></div>
<pre><code>## Balance Measures
## Type Diff.Un Diff.full Diff.nn
## age Contin. -0.309 0.239 0.072
@@ -1173,21 +1171,21 @@ statistics. It offers many options for customization, including the
shape and colors of the points, how the variable names are displayed,
and for which statistics balance is to be displayed. Below is an example
of its basic use:</p>
-<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="fu">love.plot</span>(m.out, <span class="at">binary =</span> <span class="st">"std"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a><span class="fu">love.plot</span>(m.out, <span class="at">binary =</span> <span class="st">"std"</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" alt="Minimal love plot of balance before and after matching." /></p>
<p>The syntax is straightforward and similar to that of
<code>bal.tab()</code>. Below we demonstrate a more advanced use that
customizes the appearance of the plot and displays balance not only on
mean differences but also on Kolmogorov-Smirnov statistics and for both
full matching and nearest neighbor matching simultaneously.</p>
-<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="fu">love.plot</span>(m.out, <span class="at">stats =</span> <span class="fu">c</span>(<span class="st">"m"</span>, <span class="st">"ks"</span>), <span class="at">poly =</span> <span class="dv">2</span>, <span class="at">abs =</span> <span class="cn">TRUE</span>,</span>
-<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a> <span class="at">weights =</span> <span class="fu">list</span>(<span class="at">nn =</span> m.out2),</span>
-<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a> <span class="at">drop.distance =</span> <span class="cn">TRUE</span>, <span class="at">thresholds =</span> <span class="fu">c</span>(<span class="at">m =</span> .<span class="dv">1</span>),</span>
-<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a> <span class="at">var.order =</span> <span class="st">"unadjusted"</span>, <span class="at">binary =</span> <span class="st">"std"</span>,</span>
-<span id="cb23-5"><a href="#cb23-5" aria-hidden="true" tabindex="-1"></a> <span class="at">shapes =</span> <span class="fu">c</span>(<span class="st">"circle filled"</span>, <span class="st">"triangle"</span>, <span class="st">"square"</span>), </span>
-<span id="cb23-6"><a href="#cb23-6" aria-hidden="true" tabindex="-1"></a> <span class="at">colors =</span> <span class="fu">c</span>(<span class="st">"red"</span>, <span class="st">"blue"</span>, <span class="st">"darkgreen"</span>),</span>
-<span id="cb23-7"><a href="#cb23-7" aria-hidden="true" tabindex="-1"></a> <span class="at">sample.names =</span> <span class="fu">c</span>(<span class="st">"Original"</span>, <span class="st">"Full Matching"</span>, <span class="st">"NN Matching"</span>),</span>
-<span id="cb23-8"><a href="#cb23-8" aria-hidden="true" tabindex="-1"></a> <span class="at">position =</span> <span class="st">"bottom"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a><span class="fu">love.plot</span>(m.out, <span class="at">stats =</span> <span class="fu">c</span>(<span class="st">"m"</span>, <span class="st">"ks"</span>), <span class="at">poly =</span> <span class="dv">2</span>, <span class="at">abs =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb23-2"><a href="#cb23-2" tabindex="-1"></a> <span class="at">weights =</span> <span class="fu">list</span>(<span class="at">nn =</span> m.out2),</span>
+<span id="cb23-3"><a href="#cb23-3" tabindex="-1"></a> <span class="at">drop.distance =</span> <span class="cn">TRUE</span>, <span class="at">thresholds =</span> <span class="fu">c</span>(<span class="at">m =</span> .<span class="dv">1</span>),</span>
+<span id="cb23-4"><a href="#cb23-4" tabindex="-1"></a> <span class="at">var.order =</span> <span class="st">"unadjusted"</span>, <span class="at">binary =</span> <span class="st">"std"</span>,</span>
+<span id="cb23-5"><a href="#cb23-5" tabindex="-1"></a> <span class="at">shapes =</span> <span class="fu">c</span>(<span class="st">"circle filled"</span>, <span class="st">"triangle"</span>, <span class="st">"square"</span>), </span>
+<span id="cb23-6"><a href="#cb23-6" tabindex="-1"></a> <span class="at">colors =</span> <span class="fu">c</span>(<span class="st">"red"</span>, <span class="st">"blue"</span>, <span class="st">"darkgreen"</span>),</span>
+<span id="cb23-7"><a href="#cb23-7" tabindex="-1"></a> <span class="at">sample.names =</span> <span class="fu">c</span>(<span class="st">"Original"</span>, <span class="st">"Full Matching"</span>, <span class="st">"NN Matching"</span>),</span>
+<span id="cb23-8"><a href="#cb23-8" tabindex="-1"></a> <span class="at">position =</span> <span class="st">"bottom"</span>)</span></code></pre></div>
<p><img 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" alt="A more elaborate love plot displaying some of the cobalt's capabilities for making publication-ready plots." /></p>
<p>The <code>love.plot()</code> documentation explains what each of
these arguments do and the several other ones available. See
@@ -1201,18 +1199,15 @@ covariate, similar to <code>plot.matchit()</code>. Its default is to
display kernel density plots for continuous variables and bar graphs for
categorical variables. It can also display eCDF plots and histograms.
Below we demonstrate some of its uses:</p>
-<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Density plot for continuous variables</span></span>
-<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a><span class="fu">bal.plot</span>(m.out, <span class="at">var.name =</span> <span class="st">"educ"</span>, <span class="at">which =</span> <span class="st">"both"</span>)</span></code></pre></div>
-<pre><code>## Warning: The following aesthetics were dropped during statistical transformation: weight
-## ℹ This can happen when ggplot fails to infer the correct grouping structure in the data.
-## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical variable into a factor?</code></pre>
-<p><img 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2OZP3++8VHUlwLTB5yIdoNJtOcNHjzYrxo7Rxg/UmnB31U1ggf4Tdo0RggiQhCVaihFiZtfXTgvnG+mX2HvF/gIIzjO9sOWUe2j2UQ6JdXw2t0yZMgQ33a0G/DnhP8tfjPwAbVirxf0/aijjjIBe/pSJaottkXMJ35PGH80Yv1BVcPqdxrGDP9rChEgAkSACGQWAq4moGo6FtWwiWpogs4qSOf7779vciOCCKl2zAQoXXnllaKmTRM5HPRE3VlVVRXqkN/+Qw45xO87voAQg9iFCmpBGX3vwUfEgkAQCAhRoIBAIOAEAVWqnTOH1bQaWMyQP9su+nb11Vcbgo7AExBUNQWbvJsIHrHlDqgkzA7kCXVmIEB96gZgArr+8Y9/yB//+EcTJIYAKuROVROwmQPV2BpChKrDtRvNeYHjB1F31q8uHj6szIGAPxZvEFWUDRQE/4Asg+hFIqhPtdQHFEU/1V1C1GzudyzS35/fSd4vaAvXxd///vcDDvft29f3uwRpDJYvF7+FpiRwnixG9vfX1Pk8TgSIABEgAumNgGsJKLQ5akqUESNGhCUBGiAk+A+yoH5won6BouZfOeigg+SKK64IOft4gEciwaKAEaUOAoG0M9DcQQI1e2pSj6R6X5nOnTub7WDnIcoapCGahz+SvKt53WgjoSm2mjk1FRtcAwmGryMxbEAb+e9//9tkCkBy+jPPPFNAOj/77DNDeqG1xXxibkK1i4wFsZwXqrvQYqL9QFE/YcFiACCYEHVJEPXnDCwW9XfMX7C5QzoxvABAk+6USH9/znPsNuYSLxHQYobT4oOMQgsdKBi/U/Abbur3CzwhGE+gIAsD2rK/4cDj/E4EiAARIALph0Aju0m/fsfVYzwMsRoRzKnqrxeyLvVxNJo1kBpoqkaOHGlWSsIJ0L5BoBlDGiX8j0WQvgemWivoE9Le2LyPSKMEQX5MKyAcGqVsv5pPS1RxLJiAoIDQYnUdpwALaHejNdkivRRWGdJALh/5VJ8/k7II9UOjmgiBKR4mWOAM7ShSR6lvqiC/62GHHebTQuNlAhKq3VjPCzWGoUOHGkKIlEhOAeG87rrrjLYSabfUf9V52My1+kiKBnX57W/qC86B6RuuDk6BVh4S7fw56wjcRlvQwIP0O0WDrKRbt26iPrtmN8rBTA8XCKcEmszxG7YaYVsObhtOQb0a6HWAuR0ptmCJgCaZQgSIABEgApmDQMZrQKGl0oAMM2MgesivCE0mPmHGhVYslGC5zlNPPdWQrO9973vGR/Khhx4y5knsh+ChCfJ57733Gp9ImI2jEZhO0QcsRQmSpQE2Jheo7TOIKDR8WB4UBFIj8EXT6hzwQIemCuWwBKJdatTZD5jeUbcGXhniqFHjph1oMpF7FJ/RiKbbMctEYulS+B6CkMNEbn0GQWACzdhN1Q93B6spA+mHSwDGgzoxfk1zZMYG7Z5GmsugQYOM5laj1A0hBX5oN5gAk1jOC1YX9oEAYx6wmAF8U6HxRMJ45K9Ejk70FZrPX/ziF6KrXZkXHhCx+++/3+TMxO8oGtFIegEBxG8F52I8GDfm7bvf/a7RBEdTX7iyGkhlXrTwcgFNKK4DEPgbbrhBNCpdNPLfnI7rBwQb8w+rADDA+AJJOSwI8IPFGOA/DR9TbDt9jPHbBl5YchSfP1BfYPi9amooY6UAhhQiQASIABHIIAT0QZ+RYtMw6VSZiFx8IrWQmvE8qsk0keyBAw+MgsdxfeialEq2HvVT9PzrX//ynaomSI9qb0wbyEMJQRQ8UhgFSrAoeESS60PZRKijDaQdslHG9nxEZmvgi2kDY9BE+h6k+kF5G5WNskpGfH1F9HDgeFQ76kHkv2rmzLlK2DyqxfKoNtM2ZT4RBY/UOoGiJMGkEcJ+1WiZiGWkJ0I/cEw1fx71szTfbeqpWKPgVaNr8kuqj6wZK6LYrWDsqvEz7aBtNc+aqPyTTz7ZzIUtp769fktxRnKejYIPjLq2Uek2xRXasGm50AeLASLIreiLiUmDZHOlAm+kFVIiaYuE/LTt2Sh4FFRto4mqt+0hVRSi4xFRbsVGwSM3bDyCFEuI1FfSbsam5NJkOMC8OwXXmRJMc22hX+rS4lHSaM6xUfBI66QvbH51IcIe1wlSVFkBXvoi5/t9oj7MM1J+UYgAESACRCCzEGiF4eiNnhIGAas5hQYrVHAHNJk4jkjxWAQrGEHjpwQ06OmYJgRNoX1oQUMJTNY2UjtUGWhsodlC8BH6HI+gPc1RaYJREAzTnAKzLrRoobIYhOpLrOeFqg/zBj9aTZ8VcjlLYAStKCLo4xW0B59WmK2h1U2mwN0BY0MmiHBtwZcZ10mo6wN9hKYdq0IFC1xyjsH+PqHVD1ef8xxuEwEiQASIQHohQAKaXvPF3hIBIkAEiAARIAJEIO0RcGUQUtrPGgdABIgAESACRIAIEIE0RoAENI0nj10nAkSACBABIkAEiEA6IkACmo6zxj4TASJABIgAESACRCCNESABTePJY9eJABEgAkSACBABIpCOCJCApuOssc9EgAgQASJABIgAEUhjBEhA03jy2HUiQASIABEgAkSACKQjAiSg6Thr7DMRIAJEgAgQASJABNIYARLQNJ48dp0IEAEiQASIABEgAumIAAloOs4a+0wEiAARIAJEgAgQgTRGoHnXTmxGoHQt9GZsjU0RASLQ0ggUFxeHXMoWfeM9oaVniO0TgeZFoKSkRGpra5u3UbYWMQLUgEYMFQsSASJABIgAESACRIAIJAIBEtBEoMg6iAARIAJEgAgQASJABCJGgAQ0YqhYkAgQASJABIgAESACRCARCJCAJgJF1kEEiAARIAJEgAgQASIQMQIkoBFDxYJEgAgQASJABIgAESACiUCABDQRKLIOIkAEiAARIAJEgAgQgYgRIAGNGKrMLvj555/L0qVL/Qa5bt06+eijj/z2xfNl+fLlMnHiRFPF+++/L9u2bYu5up07d0Z1rsfjkf/+97+Cz2Dy5Zdfyj//+U957bXXZNGiRcGKJHzftGnTZP78+QmvlxUSgeZCYNasWTJ27NiwzTmv++nTp8u3334btny4g4m+7hcvXiyvvvqqPP/884J7QHMI7qufffZZczTFNohASiNAAprS09N8nXv55Zdl5syZfg2CkP7f//2f3754vuBh9Z///MdU8cEHH8RMQP/2t7/JhAkTourKvn375IEHHghKQO+//355/PHHZevWrTJnzhy5+eabZcyYMVHVH0vhDz/80EfIYzmf5xCBlkYA19SDDz4oq1atCtkV53U/Y8YMmTdvXsiy4Q5MnjxZ/vKXv4QrcsCxcNc9XrpvueUW058tW7bII488In/4wx8OqCPRO3BfBeGlEAG3I0AC6vZfQITj37t3r+AmjU9oCEHWnLJ9+3ajzVu7dq1zt9nesGGD4K3fKbfeeqt06NBB9uzZY+q1x3bt2uVXd0NDg6nXakuhAUH7+I6yEGg1oWVZvXq1rcb3iTJLliwJSjxRCGN699135d5775Urr7zSPJB+9KMfyQsvvOCrAxsYA7SVth/Yt2nTJnyYh+/mzZvNNsazbNkys23/oBz6iP3AKZSgrwsWLPAbf6iy3E8EWhoBEEn83keOHClvvPHGAd0Jdt2feuqpcvTRR5uyuC5AEK1s3LjRbkrgdY9yuO5xvTrvPagD+3FfckpT1z3KwtpxySWXyM9+9jNz7f/5z3+WcePG+V2/wa57XMOoH/3AfcfKihUrfPck7ON1b5HhJxEIjkDGroQUfLjcGysC0GL89a9/NTf6wsJCY0b79a9/LQcddJAxbeONvr6+XlDu8MMPlxtvvNE0BWI3adIkyc7OltzcXCkrKzP7QfagzQBpfOKJJ+Shhx4y+z/99FP5+OOP5Y477hBoSZ988knp2rWrzJ49W0AM27VrZ9oG4auoqJBu3boJyKwlyCiLc1u1amXMXGi/rq7O70HnxCAnJ8f0C+b5UaNGCVbTOfHEE+WEE04wxVAv6kM/MW70A+0deuihpj8DBgww5Hru3LnmYfbee++Z+vCQevTRR6VNmzamnO0DTH4XXXSRnHnmmc5uCLQ7v//97w2G0ML++Mc/lpNPPtmvDL8QgVRC4O2335YjjjhCjjzySHNNXHrppdK2beMjJdR1D4tKaWmpXHDBBfLDH/7QaB3Ly8vN9XvOOefIO++8Y6wPgdf90KFDDckFqfvXv/4laOvvf/+7oA81NTWyZs0a+eMf/yidO3eO6LoHjrh/fPLJJ9K/f3/p0qWLFBUVGVKKazbcdY++gXjipRr96du3rykPsgoSivsi6sT9itd9Kv1i2ZdUQ4AENNVmJIX7A40Hbvq4Wb/44ovy5ptvCh4M8Ov805/+JNXV1bJy5UpDxK6//nr54osvBH6OTz/9tCFiv/nNb6IaHR4uuIkfddRRxjQOF4EzzjjDPKROOukkGTx4sCG/3bt3N4QXDw3c/GHm69Wrl3kgoV+9e/c252B/oOBhg4clzPrPPPOMeWAcdthh5gEJ0gztSl5eniGTOBfjBskEAYXgQQMy+frrrxsS/dJLL5kH2XXXXSfwdxs4cKApB6IKbQseUiCgeHA7Bf6nv/zlLw2eKAMCjzGCSFOIQKohAA0gXEjw4ojrHtYM+DVCuwnTdqKveyypeO655xrXG5BPaEvxgorrpn379vLWW28Z9x7cL0BEm7rugSde8mB2x70K1zpeps8//3xzD2jquocl5OGHHzYEFMQZ1yteYOHKBL953BcgvO4NDPxDBIIiQAIaFBb37YRmEeY0p4DQYb8VaAxAPiGdOnWSr7/+2hAk+ExCk/DKK68YfyqYm1EXtKEgqLaOESNGROXz+J3vfEfuueceo/kAKTz++ONN284/0JZCUA6CBxOCIqDZzMrKMkQU+w855BB8BBVoTe+77z7zMIG2FmQSAQkgpTiGhxKIJQg4SCy0GlbwgIFUVVVJjx49DPnEd2h5nO4I0ApD8CAFYYYm1Qr6jAc2iC2CsyAwQaKtfv362WL8JAIpgwBM1a1bt/YF00B7iOsGBLQ5rntYSqBthQ8qBBYHtIt7RqTXPe4RN910k1x77bXmZRH3Dbw4os6mrnsQTLwcwmICy87BBx9s+oHrfurUqWYbf4Jd9/n5+eY4r3sfTNxwKQIkoC6d+MBhQ4sBM5ZTYGIC6bRSUFBgN32auR07dhhNAjSCw4YNM2TtwgsvND6PeEBAU2LFmufsd/vp9ANz+kjCDD58+HCjUQExQwATNLCBgpu/JYI4hn5aEoy6oeXEwxL/AwWEE2TzqquuMuTx2GOPNRrXs846y+dXisAEaF+wD+2MHz/eVw0eYlbw4ItEMEZoXJwCbE455RTTV+yHrxxIPoUIpCIC0Djiesd1BoH28KmnnhK4mER63YPA4SUXEst1j+sD14lT0DZefpu67nFf+tWvfmXca3ANw5qC/7ifwXID03qk1z3ahxa2KeF13xRCPO42BA58IrsNAY7XIAA/JgTj2GhWBNyA8A0aNCgsQtDywR8TBA4azilTppjyeLDgXJA7HMf3YOla4FcJs719ADmj22Gyh9kdWg1oWVEODxfc7G06lmOOOcZoPnr27Gl8sUaPHm20i/AFQzlbH9p2El07KJTDOJ3Hv/rqK/MQBfmGFhJaXBBQtIH6AjXFtq5wn2O8UfUIMgLGqMsKtCh9+vQx+zEP8ImDCdE+nG05fhKBVEAA/tDffPONXHPNNXLeeeeZ/7AS4CUUvtSRXPcYB14UFy5caIYE0mcl3HVvX2jhdwpfaVgecM0gwA9Witra2oiue9wbdu/ebVxrQDoheAHH9Q4LBa97Oxv8JALJQ4Aa0ORhm1Y1Q9sI7cVPfvITQ3ygnYA5DQ+YcIIAAPgzXn311cZXEuZpPBTwQID/I3w2r7jiCmMWs+Z7Z30IXILm4fvf/77ANIWH1/r1600RtI3AJ/hmguiiDWg4oIVEABMeRugjzP8XX3yxCRJCG9Biov8IHvrd735ngpxAJoNpKDt27Ch33nmnMbuB9MFdAITwrrvuMtpI4IIH4g033GAeWOgfzH/RCvxBL7vsMkO0oXlxapNRF8aPICT4mIIon3322cavLtp2WJ4IJBsBBArBMhH4G4bPMrSGl19+eZPXPfoIX2j4a+KFC+4reBmFhLruEXAI8zj8vHFdw/cS1w3uQbBy4CU10use7dx+++1y9913m3pgRseLJfoEiwqILK97oEQhAslDoJWaUIJn5k5em81Ss9XENUtjGdYIgmBAwnAzj1SgwcRDIJgpCkQR2jynuRrEFCY7+ERCkF4FZmkQzECBBhXk1GlCR50oa/chJQrIY7D2cb59uAXW7fyOMaCf1kfLeQwmOdQRDSb2fGiHkOIF5yOgKVwd8AsD9pToEQBuTv/cwBp4TwhEJLnfg133uA7w0mmzQEALCWtGsGsu2HWPlzNrBUHvcb0iICiQDONYpNc96sD1jUCqQOF1H4hIen3H8wUvE5TURODAp31q9pO9akYELCmMpkknuQw8z0kK8VB69tlnDVF0thPsAWTrCUYenXWiHIhdKAl2frCy4caAIIt4JdwYbd0knxYJfqY7AoHXKKLW4d5iU5xhfHhptEGKgeMNdt3ihdNZL156g5FP1BXs/MA28B11BCOfOMbrHihQiEByECABTQ6urDUEAiCgMLlh5SG3CFK0OMm2W8bNcRIBJwLQ/MN8jrRobhBe926YZY4xHgRogo8HPZ5LBIhAyiBAE3zKTAU7QgRSAgGa4FNiGkJ2glHwIaHhASJABIgAESACRIAIEIFkIEACmgxUWScRIAJEgAgQASJABIhASAQy0gQPP0Pkh6QQASLgHgSw2tX9998fdMDI9XjccccFPcadRIAIZCYCyE2LVe4oqYlARhJQpN9Aqo/HHnssNVFnr4gAEUgoAlikAOQTCygEE+SWRfL/Rx99NNhh7iMCRCDDEEAu6gcffFC+/fbbDBtZ5gwnYwko1tBesmRJ5swUR0IEiEBIBLDC1PHHHx+WgGLBAyy2QCECRCDzEZg9e7ZZ3pgENHXnmj6gqTs37BkRIAJEgAgQASJABDISARLQjJxWDooIEAEiQASIABEgAqmLAAlo6s4Ne0YEiAARIAJEgAgQgYxEgAQ0I6eVgyICRIAIEAEiQASIQOoiQAKaunPDnhEBIkAEiAARIAJEICMRIAHNyGnloIgAESACRIAIEAEikLoIkICm7tywZ0SACBABIkAEiAARyEgESEAzclo5KCJABIgAESACRIAIpC4CJKCpOzfsGREgAkSACBABIkAEMhIBEtCMnFYOiggQASJABIgAESACqYsACWjqzg17RgSIABEgAkSACBCBjESABDQjp5WDIgJEgAgQASJABIhA6iJAApq6c8OeEQEiQASIABEgAkQgIxEgAc3IaeWgiAARIAJEgAgQASKQugiQgKbu3LBnRIAIEAEiQASIABHISARIQDNyWjmoZCOwcOFCOeTQQ6X/gAEyadKkZDfH+okAEUhxBBYsWCAjDjlEBgwcKF9//XWK95bdIwItjwAJaMvPAXuQhgicf8EFMm/xYtm8Z4+cetpp0tDQkIajYJeJABFIFALnnX++zF+yRDbt2iWjTj1VtmzZkqiqWQ8RyEgESEAzclo5qGQiMHbsWJnwxRfyvTvukOsef1xWr14tf/nLX5LZJOsmAkQghREYM2aMfPXll3LJ734n1z72uKxatUr++te/pnCP2TUi0PIIkIC2/BywB2mGwFNPPSVl1Z1kyAknSkVdFzn0zLPkrw89JHtUG0ohAkTAfQjgntCxpkYGH3+CVNbXyyGnnyEPKgHlPcF9vwWOOHIESEAjx4oliYDs27dPXn3tNTn4lFOkdevGy+f4Sy6R1arxePvtt4kQESACLkNg79695p4wbNQoadWqlRk97gkrV6yQd99912VocLhEIHIESEAjx4oliYAJONqwfr0MPPYYHxrdBg+Wyi5d5IUXXvDt4wYRIALuQGDixImyaeNGGXj0Mb4Bdx86VMpra3lP8CHCDSJwIAIkoAdiwj1EICQC8PVq266d9Dx4mF+Zg08+RV7/z39k9+7dfvvx5Te/+Y0MHjRIlmiAAoUIEIHMQgD3hHbt20v3gw7yGxisJK+9/jrN8H6o8AsR2I8ACeh+LLhFBJpEYNy4cVKvqZfaZ2f7lR06cqQ0bN4sCFByytKlS+X3v/+9TJk6Ve655x7nIW4TASKQAQh8+umnUq+plwLvCQedOFI2b9okn3zySQaMkkMgAolHgAQ08ZiyxgxGYNznn0u3of6aDgy3q5rhC0pKDvADff/9943f6CmaH/CtN97IYGQ4NCLgTgTGa0aMbmpyD5RuQ4ZIfnHxAfeEwHL8TgTcigAJqFtnnuOOGoHly5fLqpUrjbYj8GQEJPU94gh5+513/A599tln0quuVs4//jj5VhNVr9DABAoRIAKZgQAsHGs0DRs0oIHSuk0b6Xv4gfeEwHL8TgTcigAJqFtnnuOOGgG7ukldv35Bz+1/xJEy7ZtvTF5QW2DqlCkysGtXGditm9k1bdo0e4ifRIAIpDkC9p7Qpf+AoCPppy+luAesXbs26HHuJAJuRoAE1M2zz7FHhcAUfZBk5eRIpRLKYNL3sMPMbgQlQDwej0ybPl36a17AHjWdpI1qRGbOnGmO8Q8RIALpjwAIaFZurlRoFoxg0vdw/3tCsDLcRwTcigAJqFtnnuOOGoGpGkhU07OnL/9nYAUdNe0KEtR//PHH5tCyZctk27Zt0kv3t9fI+fqqKpkzZ07gafxOBIhAmiLwjVo8Ovfs5cv/GTgMLFRRqte9vScEHud3IuBmBEhA3Tz7HHtUCEzVh021EtBw0nPEcPnYGwn/7bffmqLdOnUyn7UV5bJo0aJwp/MYESACaYSAuSf0Cn9P6DFsmO+ekEZDY1eJQNIRIAFNOsRsIBMQwJJ6c1V72Um1HeGkpz5spquf50ZNTG0JaNfqKnNKXUWFLFq4MNzpPEYEiECaIICcv/PmzpWaXuHvCb2GDze+4Zs1TRuFCBCB/QiQgO7HgltEICQC8+fPN0nmO/XoEbIMDtgE9ePHjxecU6ZpWArURwzSuVw1oIsXm23+IQJEIL0RmDdvnkkyX909/D2hx0EHG39w3BMoRIAI7EeABHQ/FtwiAiERsMFD1d5o9lAFq5Wg5uTnCx42WPmoVrWeVjp17CjQgmzdutXu4icRIAJpisCsWbNMz5u6J9T07i3Z+hJKApqmE81uJw0BEtCkQcuKMwmB2bNnS7usLCmtqQk7LOQD7arLbuJhs1j9PWvKynzlq0o7mG3mAvVBwg0ikLYI4J6A1Y9KvT7eoQbiuydownoKESAC+xEgAd2PBbeIQEgEEL1eqalW8DBpSuoHDpIJX35pCGhnDTyyUllaajZJQC0i/CQC6YsACGhll/qQEfDOkXXRl9IJEyY4d3GbCLgegaafpq6HiAAQARE8bCo0n2ckUq8Pmw3r18sSTcMEv08rVR2oAbVY8JMIpDsCs/WltKJrZPeErvpSuk6T0S+mD3i6Tzv7n0AESEATCCarylwE5mi0a8QEdEDjqiiIkq30kk4g01EDkqBBXanLeVKIABFIbwSMVaS+a0SDqPfeEyZOnBhReRYiAm5AgATUDbPMMcaFQENDg6xetcqY2yKpqEz9RPMKi0zR6rJGszu+gHwiKn61rh1NIQJEIH0RQDAhNJqhVkAKHBkWqcgvKpJJkyYFHuJ3IuBaBEhAXTv1HHikCNh8npFqQFFvWX0XfEhV6f4gJHzvWFxEAgogKEQgjRGYqxYRSEV9F3xEJJ379BG7dnxEJ7AQEchwBEhAM3yCObz4EUC+P0h5XV3ElRUhMrZVK3FqQHFymWpG16xZE3E9LEgEiEDqIeB7Ka2N/J7QuU9f+XrKlNQbDHtEBFoIARLQFgKezaYPAnjYIN1KiSOnZ1O9z+2owUcej+zZu9evKDWgfnDwCxFISwRwT8jKyZHiKO4JnTUf6FLNDbxp06a0HDM7TQQSjQAJaKIRZX0ZhwAeNuWdO0eUbsUOvq36e0GmL1hgd5lP+ICuoQ+oHyb8QgTSDQHcEyqisIhgfEhID5k+fbr55B8i4HYESEDd/gvg+JtEYN68b6UsClObqTAvz3zMCFj7vaMS07Xr1jXZJgsQASKQugjALaesc21UHbTL+E6bNi2q81iYCGQqAiSgmTqzHFfCEPh2fvTajq3btkmWLsk5Y+Eiv350KCyUjRs3yr59+/z28wsRIALpg8C38+dH5ROOkWXrS2lZdSexy/qmz2jZUyKQHARIQJODK2vNEASQyxN+W0ijEo0gTUuBrv0+e/ESv9NAQD3qGwoSSiECRCD9ENi1a5csW7pUyqO8J2CkVd27yYwZM9Jv0OwxEUgCAiSgSQCVVWYOAli5BNrKaB82yB1aVF4hs5S8OqVDYYH5ul5XSqIQASKQfggsVLcavERG+1KKkVZ16y6zdFU1ChEgApobmyAQASIQGoH5amqDRPOwwcNpy9atam6rkmUacLR9505fA6WqAYWsox+oDxNuEIF0QmCBN7Aw2pdSjLGqW1dZoi+1O3bsSKchs69EICkIkIAmBVZWmikI+AioRsFHKtvU/xMktLy62pwyT9eEt1JS0EhAqQG1iPCTCKQXAvaegBXPohUsZoF7A6LoKUTA7QiQgLr9F8Dxh0UA2o6S8nKTBzRsQcfBLVu2mG/V3ijZeUv3E1Ca4B1AcZMIpCECIKAdKiulXVZW1L2v7FJvzrErKUVdAU8gAhmEAAloBk0mh5J4BPCwKYtC+4kewP8TUtWpWtq1ayfzly833/GnyJueacOGDb593CACRCB9EMBLaTQuOc6RdVCrSFu9J1AD6kSF225FgATUrTPPcUeEANKtRPuwsRrQgtxcKa+qlG8dJvjWrVtLoaZnYhR8RPCzEBFIOQTmqfm8rCZylxznAHD9l6tlhATUiQq33YoACahbZ57jjggBaEA7RqkBtQQ0X5fqK62skgUrVvi1VUwC6ocHvxCBdEIAGtBYApDsGMtqOwtebClEwO0IkIC6/RfA8YdEALk8N6qpPNqHDQhou7Ztpb3+76i+YvNXrPRrozg/jxpQP0T4hQikBwKwXGzWtdw7KomMVfBCawOZYq2D5xGBTECABDQTZpFjSAoCNt1KtEvugYBC+wkpUwK6VFMxOVc+ggaUPqBJmTJWSgSSioAljtFaRZydgvl+8aJFJhreuZ/bRMBtCJCAum3GOd6IEbAPm1g0oHmWgFZUyO49e2SFI+9ncZ76gDIIKeJ5YEEikCoI2JfSjrV1MXeprKaTYDWlVatWxVwHTyQCmYBAsxHQtWvXyjvvvCOzm1gFYt68efLRRx/J9u3b/fDdqcm8P/vsMxk3bpxgeUQKEUg2AnjYIGK1RLWY0chWTUKfn51tTilVAgpZvGq1+cQfakB9UHCDCKQVAngpbde+vZR4r+tYOl/WqTF/6CLVglKIgJsRaBYCOnnyZLnssstkzpw58vOf/1z+/e9/B8X8pptukscee8yQ1B/84AcyYcIEUw5kFN9BTJ977jlTB5L5UohAMhHAw6ajJptG5Go0gjRM1gRfquvBQ5aoGd5KofqAblI/MgoRIALphQBeSjuqCb1Vq1Yxd7zUu0AFlvmlEAE3I9C2OQb/wAMPyJ133imDBg2S8847T6644goZNWqUtNc3SSvTpk2TNWvWyLPPPmt29erVS1544QUZPny4vPjiizJixAi54YYbzLGrrrpKvvjiCznkkEPs6fwkAglHAKlSSmNIt7JVfUBrO3Uy/SkoLjZaVCcBRS5QEtCETxcrJAJJRwDR69HmBQ7sVJEubAHLCgloIDL87jYEkk5A96j/29KlS2XgwIEG2wo1XeRqfsRlmhuxXpcls9K3b195/PHH7VfzgLbr5cIsP3LkSN+xoUOHyowZM/wI6JgxY0w7KIQ2KUQgXgTwsOk8bHjU1cAEn5vduEoKNCWlZWX+GlD9/W/2JquPunKeQASIQIshgJfSLoceGlf7uCdgJaUlS5bEVQ9PJgLpjkDSCehqNT3mqcbHabIoKioSrIXtJKAwc+Z4AzdwDjSht9xyi8F35cqVUljYuIY2dmAbpNYp0JbCRA/J9vrfOY9zmwhEgwBcPBYtXChDzz4nmtNMZOs2dRnJz26MgsfJJWqGX7Zmra8emODhx4wXLP5WfbBwgwikNALIZIHo9WHnXxB3PztUVR/wDIu7UlZABNIMgaQT0DZt2sjevXv9YIGGMtSDFz42v/jFL4zPqDWxB9aB8y1ZtRU/+uijdlOQv7Ffv36+79wgAtEiAA09IlWjjYDftm2bIaF5OY1BSGgXBHSxQ9tRpFHwEPxOQ10HpgD/EAEikDIIQOmBF8fAewICbOESVqP+4nAzi0RKdIU05z0hknNYhghkGgJJJ6ClpaUCkySi2LOyGs2S0H5Wex2xnYDOnDlTbrvtNrnxxhvl6KOP9h0qUxMmzrGC7c5Rrk5jz+UnEYgEAZuCKdplOPFbh9g0TNgu0WvgKw3Es1KoJngI/EDL1R+MQgSIQOojYO8JTgIKUnrvvffKbn1Z3aOKlnPPPVdOOOGEJgeDzBpff/VVk+VYgAhkMgLRhffGgERbXQ0GAUSvv/66OXvs2LFSUlJi/mPHQjVzwhSJt0hEyN9+++1+5BNljjzySHnrrbd85ZCKaciQIThEIQJJQSDYwyaShnwE1OEGUqIvUGt1BRVrCShSEzwEGlAKESAC6YGA755Q15gDFNfz3594Qopyc+Suq34kR2qcwyuvvGKCaZsaUbHGQqxS1zLnAhVNncPjRCDTEEg6AQVg11xzjbz88sty0UUXmUAjaDmtXH311Sbt0ujRo83yhNdff70cddRR5v+ZZ55piuGNEn6kF154oSAC/pxzzvHzH7V18ZMIJAoBBBsU6ItSrsP3OJK6gxHQYtWA4kGzypt8vsChAY2kTpYhAkSg5RHAPaGwQ6lk67MI8sknn8gKJZHfP+kkydMXzrOOPkpyNLMLlCVNCfKIgsAi3oFCBNyKQNJN8AC2Tt8YkUoJ6+gWa1oap9iLFb4zIKrBBFrU3/3ud4L8ivD9xHcKEUgmAnjYlHs1HdG0YwlorkMDWtShg6li5Tp1PVFtaKH3AUYNaDTIsiwRaFkEGu8JtaYTII94dg3u0V3qvEnpszW10pGDBsp76g8KU3xgnIKz99CAQlasWCGVUS504ayH20QgnRFoFg2oBSiQfNr9kX4WFBSQfEYKFsvFhcBcTf0Vrf8nGtxPQBv9nbGv2EtAV6xfh69ifUDxQkUhAkQgPRAw9wTvS+nEiRONQuUkzVPtlEM1+BVBslh8JZwUe32/QUApRMCtCDQrAXUryBx3+iEAbUdFly5RdxwENFe19K30nxWY4CErVAMKsaskUQNq4OAfIpAWCCAftb0nfPjhh9JVF5uo9Woy7QDKNMVgrWo0J02aZHcF/Swu368BDVqAO4mACxAgAXXBJHOI0SEAV5ENmmkhVhN8njcJvW0Va0fDh3nlukYNKHLeIkqeGlCLED+JQGojgMwrmzVrRUVdF5NAHukCj1ZzezAZ1LWrIKML0riFkiy9/nPz8wU5rilEwK0IkIC6deY57pAIQNMBwcMmWjEaUIf/pz0ffqA2CAn7EIhEDahFh59EILURsPcEvJQi+AgvkEN69Aja6f7d6o0Zfvbs2UGP251Fmh+YBNSiwU83IkAC6sZZ55jDImAfNtbcFrZwwEEQ0Lys/Uno7eHCkmJZ5chli0AkakAtOvwkAqmNwNy5c00HO6jZHUnnD9Glo9vqIivBpEaJJV4woQUNJ4UkoOHg4TEXIEAC6oJJ5hCjQwAPmxwliNBQRCvbQECDaEALSzrICgcBhR8oNaDRosvyRKBlEMBLaa4Gwc6eP9/koz58wICQHYH/d6/azjJzxoyQZXAA9xcGIYWFiAczHAES0AyfYA4vegRAQCvr66M/Uc/YgiCkoAS0RFZv2OirE5HwJKA+OLhBBFIaAXtPwEIq3Ws6SWWHkrD97akr9S3XCPctW7aELFeoKdlWMQ9oSHx4IPMRIAHN/DnmCKNEYPacOVLeJTYC2ugDuj8Fk226UPPfrvYmose+Al09pYErIVl4+EkEUhqBWerPWaAR7wg+OiKM9tMOooeuCw+x7jx2v/OzsFQJ6KpVzl3cJgKuQoAE1FXTzcFGgsAcJaCVXaMnoB6PR7Zv3x7CBF8iW/XY9p07TRcKcqgBjWQuWIYIpAIC0IBu3efRFGvZMqRn8OAjZz8rdBU1+IHivFBS1LHMvITu9N4TQpXjfiKQqQiQgGbqzHJcMSGwTlMlbVRNZWV916jP37Fjh4CE5mbnHHAuNKCQNZriCZKvGtBw5jlTiH+IABFocQSwXCZSMC3fuEEO7dtP2rWJbCW+rtXVgnzCoaRAl/WErFmzJlQR7icCGY0ACWhGTy8HFy0CNnVKVbdu0Z7qWwUpMA8oKirQBNUQHwFlHlCDB/8QgVRHABYRyN72WXJUiNyfwcbQtbpKFi9eLLt37w52WAq9C1SQgAaFhztdgAAJqAsmmUOMHAFLQGMJQtq2bZtpKFgQUiABhXmuIUyAQuQ9ZkkiQASSiYC9J/Tp01s6ei0ZkbTXtapasGY8SGgwKSABDQYL97kIARJQF002h9o0ArNmzZJiTY+SW1jYdOGAEghAggRLw1TgM8FvMmUQhEQTvIGCf4hASiPw/vvvi+hqZscOGxZVP2srywWrniFwKZgUlnYwu6kBDYYO97kBARJQN8wyxxgxAiCgsZjf0YAloME0oLmaV7SNJq5ep75kkHwNQoJ2BH6jFCJABFIXAaReal9ULP11ic1oBL6iNRpoFIqA5hYWSWu9J5CARoMqy2YSAiSgmTSbHEvcCMzQ1UuqunWPqR5rgg+mAUWFheoH6vQBxT6uhgQUKEQgNRFAUOJyXa+9c30Xad2qVdSdrKuslAWavD6YtNL6CjVafu3atcEOcx8RyHgESEAzfoo5wEgR2LVrl8zXqNXq7rERUGhA27drJ23U7BZM4Ae6btNmcwg+oBASUAMD/xCBlETg6aefFtm3TwYNHBhT/7ooAV2nK6CFcrcp6NCBGtCYkOVJmYBA8CdlJoyMYyACUSKAaNd9+rCpjiDPX7CqQUBDaT9RPk8J6FqvCR4+oBASUAMD/xCBlETgiSeeMP3q1qPp3J/BBgANKGThwoXmM/BPvhJQakADUeF3tyBAAuqWmeY4m0Rghnft5urusT1sYIIP5v9pG87XwKY1XgKap2mYIKE0I/YcfhIBItAyCMAfHP8hnerqYupEpRJMWEUWLVoU9Pw8NcHTBzQoNNzpAgRIQF0wyRxiZAiAgOYWFEgHr9YisrP2l4IGNDf7wGU4bQkQUKsBzScBtbDwkwikJAKjR4+WthoklJefL0VKJGMR+I12Li8PSUALSlQDqn6mFCLgRgRIQN046xxzUASmT58unWI0taHCpkzw+WqCX+9d/50ENOgUcCcRSBkEXnpptJRr+rRYtZ92ILUgoCFM8PABpQneIsVPtyFAAuq2Ged4QyLwzbRp0qlXr5DHmzqwDRrQrOyQxaAB3dDQYJbrJAENCRMPEIEWRwCpk6ZNmy57xSPVXbrE1Z/OFRWyUV1vNntfPp2V5ZcUywYNUqIQATciQALqxlnnmA9AABHw8+bOlZqesRPQJjWgat5HkNMmR7ASfUAPmAruIAItjsAbb7whSJO0ZsNG6RQnAa2tKDfjCbYiUr76gO7ZsycoOW1xENgBIpBkBEhAkwwwq08PBGZq/k8khq+JQwO6tYkgpDzv6kpIxYQVUhCwRAKaHr8P9tJdCLzz9tvSv77evDDWxElAK5Vktm3bVpYsWXIAiHma4B6CfKMUIuA2BEhA3TbjHG9QBL755huzv6Z376DHm9q5e/duwf+8cEFIqgGFbGhozAWar7lASUCbQpbHiUDzIoDr+KMxY6S+qso0XKNENB7By2YnXREpGAGFBhSynmb4eCDmuWmKAAlomk4cu51YBKZOnWrWgC8sLY2pYrsKUlNpmFD5OkcgEvOAxgQ3TyICSUPgq6++MgGFWe3bSYneD+C7Ha907thRlixefEA1+RrkBKEG9ABouMMFCJCAumCSOcSmEZgyZYrEqv1E7fD/hORlN+b3NF8C/uR5NaDrNzeYI/k5NMEHQMSvRKDFERij2s/srCyTMi1e7acdTI0S0NVr1sjOnTvtLvOZ5yWgGzZs8NvPL0TADQiQgLphljnGJhGYohrQzn36NFkuVAGfBjQndB7QXM0nCPGZ4DUXKE3woRDlfiLQMgiMHTtWhvfpLdMWLJSarl0T0gkQUMiyZcv86sPqaBCa4P1g4ReXIEAC6pKJ5jBDI4CVSFatXCm1ffqGLtTEEasBDZeGCb5geXl5SkC3mNqwbKc9r4nqeZgIEIFmQABZKsaNGyeDunXTCPgN0jlBBLS6rMz0funSpX6jaKPBSXgxJQH1g4VfXIIACahLJprDDI3A119/bQ7W9o2dgFoNaLi14NFIvprhrQkeZbdoXlAKESACqYEAVkNDvs7Soka/z1oloomQ7Pbttc6iAzSgqBt+oCSgiUCZdaQbAiSg6TZj7G/CEQABbacPiOru3WOuG5pMaDjxoAknWOqTJvhwCPEYEWg5BMaPH28a37Nnr7TTNdyramoS1plOZaVBCWiupmKiD2jCYGZFaYQACWgaTRa7mhwEJk+ebPJ/whwWq4CAhouAt/U2EtBGEzzKb93SuG2P85MIEIGWQ2DChAnSo3Nnma05Oztr+qXWuhZ8ogRm+EAfUNSdV6xL9DINU6JgZj1phAAJaBpNFruaHAQmTpoktf36xVU5CGhT5nc0YPy9vGZ3LMfZQAIaF+48mQgkEoEJE76Qg3v1lElz50nnOCwiwfpUpSmd4KqzSZfldEqumuZJQJ2IcNstCJCAumWmOc6gCIA4zp0zR+r69Q96PNKdjRrQ0BHwth6kYlpvE9ErAd26rTF9kz3OTyJABFoGAaRImj59hlkB6VsNFqpLAgHFyFasWOE3QETCr2caJj9M+MUdCJCAumOeOcoQCCD/p8fjkS794yOg26ABzcoO0cr+3SCgzij4LVtIQPejwy0i0HIIYDEKrMsOywQk0QS0okPjqkeBBBQaUPqAtty8s+WWQ4AEtOWwZ8spgMDEiRNN8FA8EfAYxhYQUE0s35TkahqmTQ4TPJb9w0OPQgSIQMsiMEldcSBbduww94REpWCyo2rXpq2UacT7Sk355hRoQDdSA+qEhNsuQYAE1CUTzWEGRwAPnU49ekh7DQiKRxBMFC4HqK0bGtCdSjp37toluV7CymT0Fh1+EoGWQwDBiN0718iMBQukpq7OZMZIdG8qdO33lUFM8PANxcsohQi4CQESUDfNNsd6AAITvvxS6voPOGB/tDu26gMkz2u6C3eubzUkJazW1Af/UQoRIAIti8DXSkAHde0mX82ZK7X6UpoMqezQQVYE0YCiLZrhk4E460xlBEhAU3l22LekIgCtw6yZM6V+4MC42sHqKTvUbBdJFLxdD36DmuFteRLQuODnyUQgbgRwDU/95hvpU1crcxYvli49e8ZdZ7AK4AeKKHjnmvB2Oc6NGzcGO4X7iEDGIkACmrFTy4E1hQBMbnjwxEtALYGM1AcU/dqkGtC87MZgB5rgm5opHicCyUVg3rx5sn37dsnRQEIEJdYniYCWFzcGIq1evdo3oNzCxvXgqQH1QcINlyBAAuqSieYwD0Tgq6++Momm4w1A8hHQCPxIrQl+I3xGveXt+Qf2kHuIABFoDgSQDQOydcd2aaPJ5xMdgGTHUF5SbDZXrVpld0luYeOyn9SA+iDhhksQIAF1yURzmAcigFVPanv3jj8AyevDaU3qB7a0f89+Aro/ap4EdD8+3CICLYHAN2p+L9YAwXlLl0lt165JCUDCuIrz86W9rrjmrwElAW2JOWebLY8ACWjLzwF70EIIjP/iC+kyaFDcrVsCmRtBEFKOpmGCwATPIKS4oWcFRCAhCCAHaH9devOLWbOkLknmd9vRjhoJv2bNGvuVGlAfEtxwGwIkoG6bcY7XIICl7+Z/+610GzQ4bkQsAc2PwAQP8162loMJ3mpM7flxd4QVEAEiEBMCU9UE361TtSzSFEnd1CqSTCnTvJ9ODWjbdu0kS19eaYJPJuqsOxURIAFNxVlhn5KOwJeafgnSbciQuNtCEFHr1q0lu337iOrKUzOck4AyCCki2FiICCQFAbwALli4UPP4Nl6/9c1AQJ0aUAwqT/1ASUCTMr2sNIURIAFN4clh15KHwPjx4wXm8OoE5PvDAyySHKB2NPADhQkepDVLSSs1oBYZfhKB5kdgxowZptGtO3YKViqr6tw5qZ3oWFzpyS3PAABAAElEQVRkUjE5E8/nqP8p0jNRiICbECABddNsc6w+BMaNGyf16v8JEhivQIMZifndtpON5Tht4JKa3khALTL8JALNj8C0adNMows1QXx9r14JuSeEG0WpmuAhcAOygkh4akAtGvx0CwLxP33dghTHmTEIIM8fApC6Dz0oIWMCAY1GA5qTn2dM8GgcfqAkoAmZBlZCBGJCAAS0umOZTNIVkLr26RNTHdGcVOrN+7l27VrfaTkkoD4suOEeBEhA3TPXHKkXgZm6+tFmNXd1PygxBBTrwEejAc3NBQFtXH6TBJQ/SyLQsgggBVNdeYVs0ZXRevTtm/TOlBYVmDbWrVvnawsm+A1cCcmHBzfcgQAJqDvmmaN0IPDZZ5+Zbz0OGurYG/tmg9GAZkdcAXxAEYQEQTJ6akAjho4FiUDCEZiuGtCc7CxTb7dm0IC2a9NW8nNz/UzweaoVpQ9owqeWFaY4AiSgKT5B7F7iEQABrdHgo7yixlVJ4m0BGtCoTPBOH1AS0Hjh5/lEIGYE4He5XFMvbd2+QzrXdxG7UETMFUZ4Yqma3P00oIUF9AGNEDsWyxwESEAzZy45kggR+HjsWOkxbHiEpZsutkUDimxS+aZLi4m+32yDkFTzQg1oJKixDBFIPALTp083lS7QAKTu/fonvoEQNXZQk7tfEBKj4EMgxd2ZjAAJaCbPLsd2AALLli2ThQsWSK/hiSGg27dvl3379kVFQJHqZcfOnbJ7zx4NQtIoeK85/oDOcgcRIAJJRcAS0NUakd5rwICktuWsvKQg34+A5hQUyjZ9Kd27d6+zGLeJQEYjQAKa0dPLwQUi8PHHH5tdvQ85JPBQTN9tEvloNKAgoBCkYkIQkq0jpg7wJCJABGJGABHwNi1Sz4EDY64n2hNLlHDC/I+XV0iuakAhmzdvNp/8QwTcgAAJqBtmmWP0IfDRRx9JRV2ddKiq8u2LZ8OSx/ycyIOQ9q8Hv1Vy9Tya4OOZAZ5LBGJHwAQg6WIQlboMZ0lpaewVRXkmNKAgnw0NDeZMRMFDGIhkYOAflyBAAuqSieYwGxF4/4MPpM9hhyUMDktAow1CQgc2bW1cD54ENGHTwYqIQFQIQAO6SdMv9R4c/5K80TRc4iWcGzZsMKflahAShATUwMA/LkGABNQlE81hiixQ30/4f/Y9/PCEwWEJaEFObsR1WhM8ApFMHlB9AFKIABFoXgSQCH71mjXSoNdh3yHNS0CLVQMKsQQUPqAQElADA/+4BAESUJdMNIcp8u6770qrVq2k3+FHJAwOmNDatGkj2WrGi1SyNQcgZPPWbSZ4iRrQSJFjOSKQOARsABLuCX0GD05cxRHUVKiLUUDs8pvWB5QENALwWCRjECABzZip5ECaQuCtt96Seo10LejQoamiER+HBjSaACRUbDWgNghp9+7dskcj4ilEgAg0HwJ2DfgumhM4X/NyNqe0ad1aCjUY0RJQ+oA2J/psK1UQIAFNlZlgP5KKwK5du+S999+Xgccem9B2oAEtyM2Jqk5oQKF1gQkeQUgQakGjgpCFiUDcCEydOtVchwNHjIi7rlgqKNYV0fab4OkDGguGPCe9ESABTe/5Y+8jRGDMmDEmz97g40+I8IzIihkNqObyjEZAPnOUhBoCmkUCGg12LEsEEoUAVkTzeDwyuIUIaBFWRPOu/95aNaJ4MWUapkTNLutJBwRIQNNhltjHuBF47bXXpKS8XOoTnOuvQfP25UepAcVgQECtCR7fqQEFChQi0HwIzJ07V3JUC1mnJviWkKJ8NcFv2uRrOlfdAOgD6oODGy5AgATUBZPs9iFCy/HKv/8tQ04caUxuicRjM0zwUUTA27bhB7pZo9/zaIK3kPCTCDQbAkuXLpVd6nvdTaPfYZFoCSnKy/cjnCDD1IC2xEywzZZCgAS0pZBnu82GAExtK1eskGGnnJLwNmPxAUUnjLlti/qA6kpIkG1MxWRw4B8i0BwIvPDCC6L2dxlx1NHN0VzQNqABxVK+CEKEIBUTNaBBoeLODEWABDRDJ5bD2o8AHjZFuspJn0MP3b8zAVsIbML/fDWnRyvZRgPamAcU59IEHy2CLE8EYkfg32oRkXbtZMThiVuUItrewAcUYklntuYGpQY0WhRZPp0RIAFN59lj35tEAOmNXhw9Wg4eNUpaa77ORIovCX0MBBQ+oBsdGlAS0ETODOsiAqERwD1h4qRJUti1q7Rr2zZ0wSQfQRomiCWgyAVqt5PcNKsnAimBAAloSkwDO5EsBN577z1Zq6udHHbmWQlvwmorCmMgoMYEDx9QrwmeBDTh08MKiUBQBN555x3ZuWOH1Ddz8vnAzhR47xt2Pfhs9QF1BiUFlud3IpBpCJCAZtqMcjx+CDz77LNSUVcnPQ46yG9/Ir5YAmofJNHUadIwbaMJPhrMWJYIJAKBZ555Rlop+euti1K0pNgXV6v1zMkvoAm+JSeEbTc7AiSgzQ45G2wuBKBZgK/XYWd9NylNWs2FfZBE0wii4Bt0Kc726oeGpTypAY0GPZYlArEhALL36quvSpvqTlKtfuEtKW31ukcQon2RzaEPaEtOB9tuAQRIQFsAdDbZPAi8/PLLskNNbYefnRwCigcHCCT+RysIQmrwRr4jFRMJaLQIsjwRiB4B3BMQONimulqqO3aMvoIEn4GXV0tA4QOKvMIUIuAWBEhA3TLTLhwnzO8wvZfX1iVl9Hhw2ECCaBuACX7fvn2yVdOw5OlKSiSg0SLI8kQgegSe0XtCWbduklNcLKWFjctfRl9L4s5ABg1rSYEJHgFSSM1EIQJuQIAE1A2z7MIxrtC8n1h+89AkBB9ZOEFAo10H3p4LAgqxyeiZB9Qiw08ikBwEli1bJp+MHStFPXpKlZrfW+m/lpZCXUXNaj1hgodYjWhL943tE4FkI9BsOSjWrl0rEydOlC5dukivXr3CjmvWrFmSrb4xKGtl8uTJsnPnTvtVevbsKR06dPB95wYRcCLw0ksvmRVOhmv6pWTJRl3H2ebyi7YNRMFDNqsfKCLhqQGNFkGWJwLRIWDuCbrm+i5d8rK+rGX9P23PC3LzZKkSY0i2akAhIKAVFRVmm3+IQCYj0CwaUJDHyy67TObMmSM///nPTWBIKFAXLFggN998s8ycOdNXBGaJW265xZyHoBL8x9sshQiEQgAPGySeL0xioMFmDWiIxwSPvjdoJHxOVhYJaKiJ5H4ikCAERnvvCes0OLG6rCxBtcZXDSwoWM4XgqU4IdSAGhj4xwUINIsG9IEHHpA777xTBg0aJOedd55cccUVMko1U+3bt/eD+LXXXpMnn3xSioqK/PYvXLhQampq5O677/bbzy9EIBgCq1evFiy/eclvfxfscML2bVJNRVGPHjHVl+NNQm00oFnUgMYEIk8iAhEisGrVKhn/+edymio35k+bnjoENCfXBEpCyUITfISTyWIZg0DSCSgurKVLl8rAgQMNaDAt5Kr5ERrM+vp6PyBzcnLkH//4hzz44IPGfGoPzp071xDQt99+25jhTzzxRFOHPY7PN954Q6A9hSC4g+JeBN58801d5tkjQ/V3kiyBOwj+x6sB3ezNBUoTfLJmivUSAZH//ve/5p5Q0q27iBLQTimiAc1XDSgEgUgIQoJQA2pg4B8XIJB0AgptVJ5qe1q12u/wDQ3n+vXrDyCgI0eO9EEOAmEFpvvZs2cbDSpI5hNPPCFPPfWUlDluIh9++KF88skn5hT4j1LciwAIaF2fvtKhqippINjk0UX5jcvpRduQnw+opmFa3bAl2ipYnggQgQgReOutt6S+f3/ZqBHmWDgiX5UdqSA2iBHL+pZ6LX8koKkwM+xDcyCQdAKKJNt79+71Gwu0otGQxCuvvFLwH5pTCDRP0IZ+73vf89V73333+bZxAffr18/3nRvuQQDa73d1+c0jLrwwqYNGABKkyOu3FW1j2d4HIHKB5qoP6JYVK6OtguWJABGIAAE8f3BPOOaSS2W5Wt5Sxf8TXc9XEzwEGtBOnTqZbRJQAwP/uACBpAchlWoQCMyLzgh2aD+rNRFwpAJzvfP82tpaQZodChEIRACZFjYpORxw5FGBhxL63RLQ4hg1oLAImOU49drIZR7QhM4NKyMCTgS+/PJLk+qo/5FHGtevll4Bydk3q4mFBrS1RujDMmLzgjrLcZsIZCICSSegbdu2lREjRsjrr79u8BuredhKSkrMf+xAgBFWqwknOOfRRx81RZAvEeb2Y489NtwpPOZSBD766CNpp8Ft3ZOw9rsTUpjg2+gDwz5AnMci3YZG3+YBpQ9opKixHBGIDgFzT1ArQ6e+fWWDvpymkgYUq6DhZdSSTkTCUwMa3fyydPoikHQCCmiuueYawRJoF110kTz++ONy2223+RC7+uqrjX+nb0eQjXPPPVfwhohUToiiHzp0qPkfpCh3uRyBMR9/LN0GD5H2SfYD3rBhgzG/x5PMGhpQmOBNHlDvspwunz4OnwgkHIGP9Z7QQ58ZazQXNaS6LDVygKIvuH/kqTsOnm+QHF2OkwTUQME/LkAg6T6gwLCurk5efPFFgdmyWJdAcwqcwwPljjvu8NtVqImDf//73wu0n/ApzdK3WQoRCEQAgWtIvwRfr2SL+S17Vy6Jta0sBwHlSkixosjziEBoBOAT/tm4cXK8Ki+s2xZWQUolgRXFR0CpAU2lqWFfkoxAs2hA7RgCyafdH+knTJYkn5Gi5b5yM2bMECSH73HwwUkfPDSgJTEGINnOwd8LeUBzsrNMipimXFHsefwkAkQgMgSmTZsmWzTAp8fBw2T58uVSohrG7ID805HVlLxS+WqtsQQ0mwQ0eUCz5pRDoFkJaMqNnh3KKAQ+10TTkO5DhyR9XBs0kK7Ym7cv1sYMATUm+MaUMPQDjRVJnkcEgiMwfvx4c6D7kCEmAr6qNPWWbzYaUCXJkCxNWbh5c+O22cE/RCCDESABzeDJddvQJkyYIFVdu0pekb+bR6JxgFlvo2paS+I0wTcS0K3GBxR9JAFN9EyxPrcjgHtCdbdukqtuXMs1c0pVaWoswemcF38TfIFs2rzJeZjbRCBjESABzdipdd/AvtCHTRfvilvJHD0i4OFvCnNePNKYhqkxCAn1kIDGgybPJQIHIoB7Qr0uAb1dE9Djuq1MQQ0ogpAafEFIjII/cBa5J1MRIAHN1Jl12biQJ3bG9OlSP2BA0keOPLaQDqpViUd8UfCaigXCQKR40OS5RMAfAfhUz1S/8PoBA2XlysaFHqo6pJ4JHqmY7Mtndl6+LyWT/2j4jQhkHgIkoJk3p64c0TfffCNYYauLPmySLZaAJkIDukV9QHO8WR3sQyjZ/Wf9RMANCEydOtWswlc3oL8vAr4yxSLgMQ9Iwwa3Hmhpc9Stp0FX8qMQATcgQALqhll2wRgnT55sRlmnyaaTLevWrZO2mg7MruMca3vwAd2xa5dktWtvqiABjRVJnkcEDkTA3hNq+/Q1GtCCvFyz7O2BJVt2j13MAtd/jgY2whICQkohApmOAAlops+wS8Y3ZcoUqdAlWpHIOdkCAlpaVGSSSMfTFggoxKP/ICSgBgb+IQIJQQD3hMouXZTU5RsCWlmSeuZ3DNQSUKRiytYoeIhdGcl84R8ikKEIkIBm6MS6bVhff/21dOrdu1mGvU5XVCktjJ/oZmvwAcSzjwS0WSaOjbgKgcl6T6jp08eMeaVGwFekoP8nOpfrXbXNaEC9mTW4GpKrfqquHSwJqGunPrMG/o0mnK71PmySPbK1hoAWxd1MjlfbsVt9VyEMQoobUlZABHwIIAl95169jR8oluGs7FDiO5ZKG04NKEzwEBLQVJoh9iVZCJCAJgtZ1ttsCCxdutSsgFSjD5tkC3yz1qoJvqw4AQTUa4LfqtG6Wbo6C03wyZ491u8WBBYvXmxWQKpRqwheGHHdlpekJgF1BiFm59ME75bfKMcpQgLKX0HaIwBNB6SmV6+kjwVLcO7du1c6Fsef7N6a4Ddv02T0ao4nAU369LEBlyDgvCfYFEwVKUpAW7dqZczwxgRPDahLfqEcJhAgAeXvIO0RmK75P9u2aycVGnCQbFm9erVpIhEEFHlAIQ3btptULCSgBg7+IQJxI4B7Qju1KlTU1Qmu2TatW6vfdnx5e+PuVJgKkIrJEFAGIYVBiYcyDYG2mTYgjsd9COBhg+X22rRN/s/ZEtDykgRoQL0EdLOmX7EPIPfNHkdMBBKPgLkndO8urTVd2qpVq9RlplhaKwlNVbHJ6G0WD/qApupMNfZr5syZMnr06LCdvP3228Mej+fgLk3fB7eSbG8AWzx1xXoufqOFcb7UJf+JHevoeB4RiBCB6braSWW37hGWjq8YzHkdigqlXZv4L532moAeD8Ut2xuX46QGNL654dlEwCIwTV9Kq5SAQvDSWJ4AlxlbdzI+c7MaNaBZ+lLaSk3yTMOUDJQTVyf8iseOHeur8PPPP5fy8nLppoqQZAvcwA499FB5/fXXpWfPnsluLmj9P/3pT6WyslJ+9atfBT0e6c74n6KRtsRyRCBJCOBt9IQfHpak2v2rXaUEtKI4ccEMdj14pGIhAfXHmt+IQKwIzJo1S75z1NHm9NWqAR3StT7WqprlPFhAVtv14DVvKTWgzQJ7zI0ceeSR8sEHH/jOB/E8//zz5Q9/+INvX7I2Nm7cKLNnz05W9RHV+8UXX8gZZ5wRUdlwhVLXJhGu1zxGBLwIrND8fli6rqp78t880eTy5cs1nUviElrnqsZjs658kpedRQLKXzURSAACy5Ytk61K5qr1nrB7927ZoA/sjgl8aUxAFw+oAib4beqKA8nVxTSoAT0AorTc8eCDD8rbb78tN910k1x88cU+4oi81VdeeaWMHDlSbrjhBvNccQ7wP//5j/zwhz+U448/Xi699FJ56623zGEoKW677TazDe3je++9J+PHj5e77rpLoIX9wQ9+IOecc47gfCxNjf2nnXaa3HfffWKXkLbtPPPMM3LeeefJ6aefLn/+859NeRzDNYO+zZ8/X2699Vb5zne+I9B44lkLQV0LFy6U1157zdRvdsb4hwQ0RuB4WmogAE0HpLoZTPA7NF0SHmZVZaUJGzxWQ8J68MYE59WAJKxyVkQEXIiA757QvYdJwQQIEuGznUwojQVE7wMQrIZEDWgy0W6+ukE+f/SjH8lXX30lWOkqX7XbH374oTGh4/u5554r0CYOHDjQR0L/9re/yUUXXSRdu3aV73//+yY/9CmnnCJffvmltNU4h0GDBpkBDBgwwJjB586da0gh2hk8eLC004Dcs88+W0466SQB0QWBBBG+++67fQO//vrr5eabb5YePXrIYYcdJvfcc48hriiALC9///vfBW3CfxoEFdpekGVIb01tlqe/0U6dOknfOJe+pgneQMo/6YqAfdhU6sWabIH2E1JdmlgC2qAPHiSjXuiNsE/2OFg/EchkBHBPgB8lsmLM9Joqy3Tp3FSWPPUB3b59u3g8HiWg+dSApvJkRdm3HL23f/TRR9JGA+Ig0EiefPLJ8vzzz5vv0DYOHTrUmO8feugh47MMLSMIJQRktGPHjkbTOWzYMLngggvkl7/8pTH5wwcUJBPLQ7/77rumHmgwoZ3Ewibvv/++qQO/q4cfftiQ0Dlz5gjaee655+TCCy80x6E1BRn9+OOPZcSIEWYftKO//e1vzXYvTXF44oknGi3oqFGj5H//93/l4IMPjtsMTwJq4OWfdEUAvjDlNTXSvhmiAZHwHlKtN4NESZbenGCCr6ssNG/IiaqX9RABtyJg7gmdO0s7DfJbs2aNIaMd4ozWTTaWOXr/AkmAlSWbPqDJhrtZ6z/ooIN85HPnzp0yZcoUqaqqMuZt2xGQU2hJIXfccYcheq+88orgZQrl8XKC30YocWpGoQHtrL9/aECtgMBaEzrawW8NGlXUbQXaWRyzBHT48OH2kNTW1prtRMcpRGyCp0nANxfcSCEEZikBrWgG7SeGvGTJEpOAPlsv8EQJluNs9AFlEFKiMGU97kYAD217T0C0Msgn8oCmssAHHIIHPFZD4vM2lWcrur6VOixmmFekTwLZQwYU+x/aRZjNIQ888IDU19cb/0pkcIB2EhH24aRINfxWw4pysAA4UyThuxUEMYGwZnmzsNg+XHvttdKvXz9bzJjZ7ReUgYC4JlIi1oBC1XvUUUcZJ1f4FDgHm8gOsS4iEA0CeNj0POaYaE6JueyiRYukc3nitJ/oCFZD2qhBEyYPqNcHLOYO8kQiQAQEL6V9TzjRIAENaKqb39FR+IBCYDbN1tWQNqnvHSXzEIAmEsSwurraL2Ie5nNoLqHp/MUvfiH33nuvgBBC4JN5ySWXGOKK75ZMxkoGu2t6Mpjp4QoA/08I2nj66aebPa1TxK+FL730kgEOPgM1avK85ZZbBMl+KUSgpRCAOWOxksKqrsmPgEdEIUzwdZr7LJGCIKQG5gFNJKSsy8UIwEy5VC0Vld60S2sNAU3dFZDsVOEFFAINaE4BfUAtLpn4efXVV8uzzz5r8niC+CGfKFIaQVsPzSQ0psg3DU0pXkiuu+46wbPOmuA7eLOwTJw4UTZt2hQ1RMcee6zAp/M3v/mN4XCoFz6dIL5OrWm4itFHpD+0Zv1wZcMdi5iAIu8VIqMADEL20Th8G+AUi6itWIAI1zEeIwJNIfDtt98ak4B92DRVPp7j0H7iZlFflQQCulXTMGkaFryVguhSiAARiA2BefPmNd4T6ruaCtZqcEZZUfyrlsXWm8jPylVzKASEI0eDkGiCjxy7dCuJFZIQSATTeoGm3EKkOxR6iIiHFhQR6S+88IIxuyPZO8z1KD958mQzVJBE+HcirZMNEooGA7Rhg5QQSV9WVmai3EGKsR2JnHnmmfLyyy+L0080kvMCy0RsgrcnIqILYCBFQBeNMnzsscfk5z//uWHPP/7xj43fAgZIIQLJRgDpJyAVXZKfZBptwe0k0RpQJKI3aZiyc8xYoAGBPw+FCBCB6BFAhC+kQn3oQOLwUleqK5elulgTvPUB3dLQkOpdZv8cCEAZEkzefPPNA3aDQyEKHYo8pDmCRdkp3/ve9wT/YXEDAYVWNFCQFxS5YpFHGs8lmOidAtc0pyBJPv5bgQZ03LhxRnEIpYfTTxXLewaa92G2d+4D10Oe0ngVJhFrQNFxvF1CVWtzRyEZKZg6loZC+P6rr75qjttB8pMIJBMBPGzatW8vpZqPLNkySzX+XaurErIEp7Ov8AHdqQ/JLO9LGx5AFCJABGJDAC+KyIhRqj52SE0DKU3xCHj0sa2SiHZKNIwGVDVe8AWExYWSuQhAURdIPp2jxbFg5NOWgfY03lgcKDuc5NPWHclne332ggDHIwdS6xC1HXfccSaXVZ8+fUyWfDBupBKwAnP8WWedJdOmTbO7+EkEkooAHjYVdXUmkjCZDcH/Zq6+fI065JCENwMfUEhrb5QiCWjCIWaFLkLA3hMQqAGfOkg6EFD0E1pQENAKNcFDoOEqTvE17E1H+YcIxIhAxAQUiVKxrJPNERWsPTjLxsuIg9XLfUQgGALQgJY3g/kdwXYwNQzo1uhXFqwvse6DCb5RGtNbkIDGiiTPIwJiljosV9cwCDSg0CoWaqqzdJBc73K8OZUVprskoOkwa+xjPAhEbIJHwlJnjijb6FNPPWUy9eM7kp/Gqs619fGTCESKwBxoQOu7RFo85nJYKq1So/4SuQKS7YwloB5pzNOG5dkoRIAIxIYALBXw/4RAA5ou2k/0F5Hw0IBiJSQIA5EMDPyTwQiE1YDiwYvF7iGffvqp/PGPfxQ4qFqBjwr8PuETSiECzYkAbtQrdGnMk7wPm2S1Df/mqVOnyhlHHJ6UJqwJfu++Rn8vakCTAjMrdQECeHlbpVlaKr1WkfWqAe1QWJA2I0ckPK5/pGGCkICmzdSxozEiEJaAIvLppptuMjmoEE34xhtv+Dm9wom2i5o7sC4phQg0JwI26rCirktSm0W6CpjxDtd0FckQqwHdt48m+GTgyzrdg4DvnuB9KYUJvntF+BVkUgmdXF0PfqP6fVoNKEzwFCKQyQiEJaAwp3/22Wdm/Keffrr885//NDmpMhkQji09EECwAcSa2xLRayTkRfQpogsRxPD+++/L559/Lucfd6zYPH2JaMdZB6LgIbv37DafNMEbGPiHCESNgO+eYH1A16+X4T26R11PS52QAw2opuXJ0Sh4CDWgLTUTTbeLFEnICpQM6aRZXdxiVQ5LQGGCRxJU+H7efPPNgu/BpKKiQvr37x/sEPcRgaQggIu/nd6wOzgyMcTaEDQNzz33nHz99de+KkBAkffsmCGD5ejBg337E72BteAhu3Y3JqCnCT7RCLM+tyCAe0KWvtCV6PMIL3Kw2nUoSP0coHZ+bBR8jr4AQ6gBtcik3ieWI8eiPHZZzET2EHlCl+hqXm6QsAT08ssvl4EDBxrNJ5aKCrXaETL4jx492g14cYwpggAeNhW1tXHfAJBi6b777pPNGzfK2UcfbZJWb1Y/rL1790l9daV0qdyfaiwZQ0ce09atW8tW1b62V5cWEtBkoMw63YCAMwXTetV+QtLKB1Sj4GGBQR5T3BOoAU3dXy1iYfAby9P7N1y0EiH7PPtk8/YdAtdHt0hY5LD0k2X4q1evDokJLhYKEWhOBPCwSUQKJiwntkbXi77lwgukc8eOzTkEX1u5qgUF6c3XlEw0wftg4QYRiAoB3BM6al5gyH4Cmj4a0Bz1AYXAFQhmeGpADRwp/eeJ234h544cmZA+LlX3i/rvnpOQutKlkrDMEUFGNhM/st6H+m/LpMug2c/0R8CkYOrS+LCJdTTLli2TsWPHmgTzLUU+0XeYXBpU84E0LNSAxjqbPM/tCJgUTF7/TxBQKE+K0yQHKOYuTzWgEBMJrwSUGlADB/9kMAJhCahz3Pv27ZO3335bdu3aZXbfe++9ArP8k08+6SzGbSKQdASgIViu5NEmnI61Qfyei/RGf9xBQ2KtIiHnZetDsgEaUCWiJKAJgZSVuAwBpGVbuWKFVDgIKK7tdLLOIQoegrFQA+qyH7BLhxsxAf2f//kfOfXUU2WFXuSIhr/11lsNZFj96MUXX3QpfBx2SyDgS7fifdjE0gf4M3/11VdyjAYYtWsT1hMlluqjOgeR8A3btpul+GiCjwo6FiYCBgHfPcGblg0a0A7efJrpAhGi4CHwA83SZPTUgKbLzLGfsSIQMQF95JFH5M0335Q69bF59tlnZdSoUYIcib/97W/lhRdeiLV9nkcEokbApr+IJwco0iu10pYPG9Av6vYTfUKW+n5u3rbVmOCoAU00uqzPDQjYe4K1ioCAlqRRBDzmKCe7vZkqsxqSkmcSUDf8ct09xogIKC5mOEQfc8wx5u1szJgxctZZZxnkunXrZpY8czeMHH1zIoCHTVv1Ty6tro65WRDQAV27SkGOXYs95qriPhHJ6Ddt3Sb52Tmyhcmn48aTFbgPAdwTkFHCpmVrJKCN+TTTBY28ABM8CWi6zFzz9xNuaP/5z3/M4kDWLbL5exF/ixER0JKSEsnVh+Qnn3wi//rXv4wf6EknnSR79uwx5vdjjz02/p6wBiIQIQIwt5V3rpXWbdpEeIZ/MeRYQw634X17+x9ooW9YjrNhuxJQ+oC20Ayw2XRHAAS0XNOywecTS0SDvJV482mmy9iQhg39NxpQ9QvfpGOgEIFABGAlQ3pMZHC56667jDUaOavTUSIioIgmhA8okq9eeuml8tOf/lQqKyvlnHPOkQ8//NB8puPg2ef0RKAx3UptzJ2H72eWakv6JXkd+Ug7CA3oZtWA5uVkM/VKpKCxHBFwIDB37jxNwdTF7IF/Nx7I6UZA0XlfMvr8AprgzWzyTyAC999/v0AB+PTTT8unn35qUve98847gcXS4ntEBBQjQdDRxIkTzdKEDzzwgBnc9ddfb5ajAhunEIHmQgDpVqyvVyxtTpo0UQaq+b2lg49s30FAt2jkax40oLqCC4UIEIHoEJg7b64vAn7Dhg3m5LQkoBqIZDSg+ZoZg+440f0IXFJ6ypQp4rQ6Y3v8+PFpOfqICShG17t3b6nSpQ9h7oAWqqamRpYvX24i49Ny9Ox02iGA5fWWLF4ssQYg4fe6evUaGZxCa0QbE7wSUJjgt6h5hUIEiEDkCGA1s2W6NrczBRPOLtE0TOkmiIRHFHyOakAbaIJPt+lrlv4uXLhQSktLfW116NDBuJT5dqTRRsT5Z+D7ecUVV8hGXbIwULgUZyAi/J4sBBYsWCDISWsfNtG2g/Xe26rvaN84UjhF22ZT5aEB3a3+1FnqA0YC2hRaPE4E/BHAPQEm93LvKkh4RrVRX8qCvJYPMPTvadPfcr0a0JySYgGxxgs3FoShEAGLAH4PiL+xgt9Ifhq+bKH/ERPQa665Rs4880y5+uqrBYzbKek6eOcYuJ0eCED7DomHgPauqzVkL1VGDA0oBMQYDuZ4mNolcFOlj+wHEUhVBPbfExpXRoMJvlgDkFqZRGup2uvg/crV1ZA26D0gp3NnUwDBVE5tV/CzuNdNCFRr9hcE0VrBds+ePe3XtPqMiIDiIsBa8HfffbeUl5en1QDZ2cxCAA8bRIqWqftHtILghEWLFslFJ54Q7alJLQ8NKMQ+MGGCQ9YJChEgAk0jYO4J+vJW1qnxnmAIaJpqhGCCX75ps1kJCSOHHygJaNO/ATeVgCIQAUj4hLb/jTfekKuuuiotIYjIB7SwsFDqNWIY0cMUItCSCOBh01HJJ/KARitTp041pyD/ZyqJj4C2Rmp8MVGNqdQ/9oUIpDICuCeUq8awTdtGfcoGJKFPYwK6TV9As739Zy7QVP7ltUzfLrjgAgEng9bz0EMPlWuvvVb69u3bMp2Js9WINKBo484775Qbb7xRlqqzN8hoW+/FjmPQivbr1/IryqAvlMxGoDEFU5eYBonowTpNH1akOfZSSawJ3vYJy3HS0mDR4CcRCI+AyYrh9f9EyfVqgu+WQkGG4XvvfxTrwTcGITUGUJGA+uPDb2J8gl966SWj/YT7o5OLpRs+ERNQ+IDChBlM1csgpHSb9vTt7zxNQl874pCoBwCH/pkzZ8opI4ZHfW6yT8jxEmKbTJjrwScbcdafSQjgpbTbkUeaISFAEaStRKPI01FystqbhV7ae11wSEDTcRabp8/FxcXN01ASW4mYgK5atcoERwTrS5sYV6QJVhf3EYFQCGCFk4Ua8Trs/PNDFQm5f8aMGSZycGC37iHLtNSBbE2/BNm7b6/5JAE1MPAPEWgSAUQDL1a/7sMuucSUtUnoi3Ut9XSUHA1CgrTyWhhJQNNxFtnnSBGIyAcUlWWpczQ0NK+++qrcc889grV2p02bJu11RRmmiYgUbpaLB4HFmv8TD5xYIuAnT54sHfWNsbpsf/60ePqSyHOtBnTPHhLQROLKujIfAQQV4sW0vLYxAt6mCSxJUwIKEzxknwZaQkhADQz8k6EIRKwBhfny5JNPNuH/MHOcdtpp8utf/9qY5V955RWzNGeGYsRhpQgCCDaARJuEHg8oBCAdkaJ+yrAg4EUOuUAh1IAaGPiHCDSJgO+e4M3ra1dBKkrTICTkAYXsQv5PvSeQgDb5E2CBNEYgYgJ6+eWXm4irxx57TIYMGWKG/NRTT8nZZ58tzz//vAlQSmMc2PU0QMA+bDrWRrcOPF6e4Ng/pGfqmd8t7IiE36kPHQgJqEWFn0QgPAK4JyBnbrn3ngANKL4X5aZWoGH4Uew/ijRMEBOIpLlMSUD3Y5OKW3954UX51wcfJqRr2zVOwW0SEQHF2rQTJkwwuacQ/m+loqJCsB78448/TgJqQeFn0hDAw6ZMk/C2z240U0XaENKHddDfbZfKqkhPafZyMMPv3LXLtEsC2uzws8E0RcDeE9qqthACDWihXkvIFZyOYgkonrm5qsUlAU3NWURQK+SL2XPM/0T20rqRJLLOVK0rIgKKMH9c0FilJVCmT5+ethd74Fj4PbUR+FYj4KPVfmKZMvh/Htm/f0oPDhrQBuT/Uw0ICWhKTxU7l0IIgICWO5bVxcM7XQOQACtWQoJAA5qtkfwkoAaOlPuD+cECAclYsc5NQd0REVD4p40cOdJoORGABMEb2j//+U955JFH5NZbb025Hwg7lHkIzNF0K+UDB0Y1MPh+7tixQ4b16R3Vec1dGLlAN2/dJvkaEU8C2tzos710RQA5QKuGDvV1H8GxJXnpGQGPQbRr01ZAQPB8RTJ6ElDf1KbUBlIgzZ8/X9ooN2qVqCxAGuS9R59V3bunrqtYoichIgKKRmFmP+uss2T48OGG9R977LEC7RKy8l933XWJ7hfrIwJ+CCADwwK94Aecfrrf/qa+jPvsM+nUsaPU6P9UFkNAt22V/FwS0FSeJ/YtdRBAMCzuCYPP+q6vU5vUBF9T29n3PR03ctXFyBLQTZrTlJK6CFzz5wfk6DPOSEgHVy9bLteMGJaQutKlkogJaLX63o0fP14++eQTmTVrlonaHTx4sOA/hQgkG4Hly5cbTaZNtxJJe9CGTNf8n+ccc3QkxVu0DHxAN2l/C3JyqQFt0Zlg4+mCwLJly0zSdmdatg26WEpxfp90GULQfiISHgQ0R1NJbdL7HoUIZCoCERNQAAB/h6OOOsr8z1RAOK7URAC+XhDnw6apnuJlCeasEWmwTi40oKtVA1pWVCQNDQ1NDY3HiYDrEbD3hHLvMpxwXUGe4OI0XQXJTigCkUwUvI5jGTWgFhZ+ZiACERFQBBqNHj1asJoMNFE1NTUyUH3xrrzySq5ZnYE/ilQcEgKQIPZh01Qf4R4yduxYGd67t9jcek2d05LHc5WAbtqyVep1rXoS0JacCbadLghYAlrhJaA2erg4TXOAWtxz1a8QBLScaZgsJPzMUASazFVx3333yUEHHSR33XWXLNBlEEE+sSINgpHq9MIHMaUQgWQjgIdNkUYd5kT4cPn888+NKfs4R4BCsvsYT/3ZaoJvULMbTPAkoPEgyXPdggBeSovVtztLX94gNgl9OkfBYxw58AHVjDPGBK8uBRQikKkIhCWgY8aMkZ/97GfmP6LxkE/xxRdfFDzcQUIvvfRSufjiiwXLoVGIQDIRwMMmUu0nVj565523pX/X+pRcejMYTrlKQHdobjloaxtodgsGEfcRAT8EcE9wuuRkigY0J6t9ow+omuBBRBFsRSECwRDAIkBwO0lXCUtAkWJp1KhRcuedd0p2QPLvIvVVe/jhh6W+vt6kY0pXANjv9EAA6Vac+f7C9RrBcmvXrpNTDhkRrlhKHYMPKKR9+3bUgKbUzLAzqYoA0rJ19K4Bjz6CgCKCvL3mrU5ngQ/oNjXB56gJHsJUTOk8m8nr+0MPPSQXXXSRyUaUvFaSW3NYArp06VIZNix0WgAEeAxVEydM8xQikEwEjAbU8bAJ1RZ8P19//XUZ0LVrSq98FNh/JKKHtNNrinlAA9HhdyJwIALIw+i0isAEn+7+nxhlbla2dynOxnymJKAHzr2b90DjiZSY/+///b+0hyEsAd2k/ifl5eVhB9lRfXCs6SNsQR4kAjEigHRKm/Fb9AYbhKvmvffeE/xuzzjyiHDFUu4Y0jBB8FLXoNG8FCJABEIjsGbNGtmi2SKc94SNGUJAYYLfpcvyZnnXsycBDf07cOMRKFlOPvlkkxIzGSsxNSemYQkoOtLUAJs63pyDYVuZiQC0nxDnwybYSEFU33rrLTli4ACp1oCldBL4gELaeJe8ReJ9ChEgAsERgPYTYiPgsb0+UwiodznO1uqOA8ELNYUIWARydLW8H/3oR4Il0tNdmhzBa6+9JkuWLAk5TgQkwQ+UQgSShYCPgNbWhm0CS8PC/+v0ww8PWy4VD1oTvO0bzPAFXh8wu+//t3cd8FFUT3iAFNJ7Qgm9996kV6UrIiCgdBREBFQERBRBxb8K2BFFRAEbCiJIUUEpAlKl9957J/T/fC+3xyVckrvk7nK7O8OP3N7W976393Z2yjfyKQgIAokIWOcEG68IPHER+fPpHiK44JVwWU6IKKAKBvljQARSVUBBubRjxw71P7W+V6umn2SP1Poh27wTAVg7QLUSlko5TSQebdq0ibo1b0pByRLmvLNXSVulueA1wyeomEQBTYqRfBMENASggOI3ExIZqVbBZQ3uzDAHadq083jjp8ZbfIe9IRBxwXvjKEmbXIFAqgro/PnzXXENOYcgkCEE8LCJS8X6efr0aQIdRfnChalKseIZulZmHaxZQO/eTaRcES7QzBoJua4eEMBLaVy+/NamahygEQbwGiALHnKX48EhYgFVMMgfAyKQZgyoAfssXdIZAlBAo1NQQK8zdybowrL7+lKnJo111rN7zc3KDxt/ttzeYg5TiCig97CRJUEgOQKJc0Ie62otEdYIWfCaAnqdrbp+PCeIAmodZlkwGAIeU0BhpVqwYEGa7nzgu337dtq/f38SqKFoLF++nP755x9d814l6ZR8cQiBPWztiLVDwYRswAkTJtCJ48fp6VYtdel6twUAiUg3LKTCooDaIiPLgkBSBDAnxNi8lGoWUCMooIGWJCSEFASyRVcU0KRjL9/uIYAiBUhK0qt4RAFdv349devWjXbu3EmDBw+mmTNnpogXOEWff/552rZtm3Uf/BC7du1KixcvpqlTp6pzSJawFR5DLyC26wjz0cbkuWftQIcRFzV+/HjawS8rvVq2oLxxcbrHITAokK6zUg0RBVT3wykdcBMCMEYcPXKEX0rvJSXCAurLCYh6jP9ODpMvJx/5sEfkiirHGSIxoMkBku+GQSDVGFBX9RKKAqoplStXjtq1a0c9e/ZUFZb8/PySXAIZ95MnTyZUWbIVlP9EotOAAQPU6qeeeopWrVpF1atXt91Nlg2IAEq+4mVDs3agzCYs4eolhuMl+7VpQ8XyJlVO9QoDkioSWOGGSOKBXkdR2u1uBDTvWEyeewooLKBGiP/UsEM9eLGAamjIp1ERcLsCCtZ+VFQqW7aswjCOLVWBnNF8hN9gk9M3wZQ8adIk+uCDD5Lwj+7mMoxNmjSxjgGqL23dujWJAvrjjz/SLi7NBhHrqBUq3S9ofH+wdiDLHS8jIKGuULQItatfn8Is/Jm67yh3AMTT19i6AxELqIJB/ggC9yGgVd7TXkqxg1GqIGmdRSb81atXKbu44DVI5NOACLhdAT158iQFsZJgS1gPCydIw5MroLZKpq0SeZxj/EJDQ63wYxlKra1s3LiRwEkKSW5Ztd1PlvWFgPawWblhAy3gKkeFcuembh076KrMpqOIIwb0AltysvPDRxRQR1GT/cyGgPZSGs1zgSbnzp2lnCGJpSu1dXr+RCISLKCoB68lWOm5P9J2QcAeAm5XQFFaEG5TW4FVNLsTXI3Jz4Hjkwfejho1ynoJuC9LlSpl/S4L+kUACmggv7BA+WxWozo15/9Z+J8RBS7401z0IZQ/RQE14ghLn1yBAFzwkTlykC8raZqcO3uOSuTIqX3V/Wcgh6fBAookpPNHj+q+P0btwLyvv6Y1i/50SfcSrl5zyXn0dBK3K6BRXBIRwdQIHPe3TBiwfubKlcthnKKjo5XFVDsAx+dJlpSibZNPYyGABLYEVjiblC9PLWrUMFbnkvVGWUD5txISGCAxoMmwka+CgIYAXkptkxJhkLjElcMijGQBZQPNOVZAo0LDaD8nWIl4FwKaUW33qpW0e5Vr2wZ9ySzidgUU9UqRQDR79mx67LHHaMmSJRQREaH+A2S8zebgt9nULKK1a9dWNb7xiRKFoGIaM2aMWcbI1P1cyclm/mHh1KZubcPjAAvoRZ58YsNziwJq+NGWDqYXAVAwRefPbz1cc1EbgYJJ6xRc8Ee4BnwAPyuFhklDxXs++/fvT+CitQ0tdFXrYLQzi7hdAQWQffv2tdIvZeXyYiNGjLDi26dPH6VMIkM+JWnUqJHKfH788ccJx3fo0OG++NGUjpX1+kVg3bp1ShGrUKUKgZrE6AIL6GW2egQHZBcF1OiDLf1LNwIwWtSpVct6vMYBGhEaYl2n9wVwgV7ll9HAfHnpEoeUiXgXAt27d/euBum0NR55qufLl09lL+NNNTw8PAlU8+bNS/IdX0aOHJlkHayoiPFEXBxiP/FdxPgIgL6LKQ2ojEnieQO5jjWS75ABe5GtHyKCgCCQFAF4wM5xCFZ0/D3qNasCGmwgBZTngKsWInqQjaPfwQaoc590NOWb2RHwCBG9BnJy5VNb7+hnCAdki/LpKFr63g8TLqi1ILE5jZNckNqowAUPye7nL2631ICSbaZFANZPSEx8vPrEHyigeC4E67gijLUzloVAjgFFbKsfU7NBtDADy2b5EAQMgYBHFVBDICad8AgCiBlOSEhQ14o2QJUjR0CDCx7i65ONLrO1X0QQEASSIqApoNF57imgSEqNMJh1UKsHn81SllMU0KT3gXwzBgKigBpjHA3Xi59mzKDcMdGqX5ExMYbrn70OwQUPAe3YBYn7sgeRrDM5AgcOHFCJH5E577GowAJqpCpIGGKE4UCy+PiqT0lEUjDIH4MhIAqowQbUCN1B/fcFCxdS3tg4ioiMJN9kJVuN0Ed7fdBc8D5Zs9FFsYDag0jWmRwBWEDBAerjm6iYAQ7EhBpOAdV4si35DmIBNfmNb9DuiwJq0IHVc7eWLVumuGMRBxUVG6vnrjjVds0CCpp9xMAi+UBEEBAE7iEAC2iUTQUkbDlz5oyhOEDRJ80Ff5dZXyCigCoY5I/BEBAF1GADaoTuLGTrZwSXW7107SpFmiT+E+MWEBio3It36a4aRlT0EhEEBIF7COxPpoDCW3KFqcsiOUHVSKK54G9ZOiUKqJFGV/qiISAKqIaEfHoNAov+/JPqlitLB06cpCgTKaAgNQ5kJfS2xfIpCqjX3JLSEC9BQFlAc9nWgD+nWoYXViOJpoBeZwUbnhFRQI00utIXDQFRQDUk5NMrEIDStZYJ6OuULUsnObYryiQJSBr4eNjcunVbfZXEAw0V+RQESJVzPnniBLvg7yUgIQMeEmEgDlD0BwVX/Dn2HWUZA8PCRAEFKCKGQ0AUUMMNqb47tHz5chX7WJhpVkDKHmmiGFCMHBTQ67duqkEUC6i+72VpvWsROHz4sDphlI0FVFNAIw1UBUlDDVbQqxxeEMTWXbGAaqjIp5EQEAXUSKNpgL4sXbqUwjmeSwvCN1MSEoYPmfAJ7HaDiAKqYJA/goBC4ODBg+rTNgkJCijmiuwGZMpAEuY1roYUECIKqPwEjImAKKDGHFfd9goW0OolS9LhU6dUH8ymgAay8n3lWiIBv7jgdXsbS8PdgIBVAc2V1AUfabD4Tw06KNZwwQdw/7Ryo9o2+RQEjICAKKBGGEWD9AGl51avXk01SpWkQ5yA5M8WgCCDZbemNVSohnSJrR4QUUDTQku2mwmBQ4cOqRCVQBuFExZQo2XAa2NqdcFzDOhZJtsXEQSMhoAooEYbUR33Z/PmzcrlVI0toIdOnqJok8V/YugQA3oBVg9WvkUB1fHNLE13OQJQQKNsrJ+4gOIANWD8J/oWxGU4ryIJSWJAAYeIAREQBdSAg6rXLq1atUo1vXLxYuyCP0nhJsuAR+eDQLnCVZDC2BIqiQd6vZOl3e5AABRMETYlOJGkCAtoFMdIGlHwEgqO08AwiQE14vhKn5jtQUAQBLwFgX///ZeK5ctLoax8HWQLqFlqwNviDwvoJX7ohAYFigXUFhhZNj0ChzgLPjJXTisOSNK7ffs2GTUGNNA/e2IWPLvgL5w/r1hBrJ2XBUHAAAiIAmqAQTRKF1azBbRKsWKqO4dPnqTI6GijdM3hfiDmFZad4IBAsYA6jJrsaAYE4IKPyHFPATUyBRPGEy74hIQECmQLL8ryXmLPiIggYCQERAE10mjquC+gG9mybRtVYgX0Kk+6F7gWulktoBjGAKaVkRhQHd/Q0nSXInCZ54NLbPG0jQE9ffq0ukaUTVKSSy+ayScLYAsoxCcgQH1KJryCQf4YCAFRQA00mHruyoYNG9RbfqViRa0UTBFmjAG1ZP1n9/MVC6ieb2hpu0sR0EjoI3PmsJ4XFlCfbNlUyI51pYEWAtkCCsnq76c+RQFVMMgfAyEgCqiBBlPPXVmzZo0qP1eucGE6cirRsmFGFzxiQCF+PqyACvWKnm9pabsLEdAUUFsXPDLgIzk+0qgSxElIkCw8F0BEAVUwyB8DISAKqIEGU89dWcf130vkz6eqmmgk9Ga2gGZjy45kwev5jpa2uxIBTQGNzHkvBvQ0F6uINigFE7BDJSQlvj7qQ4t5TVwpfwUB/SMgCqj+x9AQPVjHFtAKbP2EHOEHC0joQcpuNgENEyRr1ix0jjNfRQQBQYAICii8AwGW3wcwOcMxoJGhxreA3s6S+JgWC6j8EoyGgCigRhtRHfYHmZ5IQKpQpIhq/RF+sESZMAMenffx9VXKN90lRcFy8+ZNHY6oNFkQcC0CUEAjctyL/wRTxGmOAY1mjkyjCkpxQm5whTg/fiEXC6hRR9q8/RIF1Lxj7zU937Rpk+Lzq1DUooCyBTTMpAooBiWYE5Fu37mtxkfc8F5zm0pDMhGBI0eOJKFgAgcoSvdGGdgCmi1rVvJnNgzUgw9GOU5WuEUEASMhIAqokUZTp31Zv369ajkSkCCH2QIaYWIFFFygN28lKqDidlO3hPwxOQIgoQ+3sYBqFEzR4cZ1wWPIkYh0lQtTBIWHSxKSyX8DRuy+KKBGHFWd9QkJSIXzxFNIYKBqObLgza6AJty4obAQC6jObmZprlsQgAU00o4CalQOUA1EJCLBAhoUFq7q3mvr5VMQMAICooAaYRR13of1rICWL1RI9eIWl9Y7zck3EVFROu9V+psfyBbQK9cT1AnEApp+HOVIYyCAOGhkvEfkiLN2CBZQuKeDLSTt1g0GW4AFFApoIFt6z5wRF7zBhtf03REF1PS3QOYCgFrOGzkGtLwlAek4xzkhwSDcxAooXPCXuTIUROK+Mvf+lKtnPgJHjx5VjbBNQjoFCiYDc4BqqAdyIpKKAY2IoDNnz2ir5VMQMAQCooAaYhj124nt27eresflLfGfR9n9DjGzCx5JSBcuX1E4iAKqYJA/JkYA7ndIeNw9C6hZFNCgALaAchnSYI4BlbnAxD8Cg3ZdFFCDDqxeuqUlIJUvYuEAZdcaxMwW0GCubX2Os3xDg4Mk8UAvN7K0020IaBbQyLh7NEyKhN4MFlAtBhQKKFd+EhEEjISAKKBGGk0d9gUJSLm45nssu5ggx86cpixZslBYZKQOe+OaJsMFf53j3sKDQ8Tq4RpI5Sw6RgAKaFamJAq1MGMgJvT8hQsUY/AMeAyZigHlLPjg8Ai6xmE5169f1/FIStMFgaQIiAKaFA/55mEEVAJS4cQEJFz66OkzFMZv+yhFaVaBBRQSGhggCqhZbwLptxUBKKDh/JKa1TInoAY8JJozw40uyIJHnHx2fimFaH03er+lf+ZAQBRQc4yz1/ZyHXOAahWQ0Mhj/HAxs/sdGARZFFBUQpEseCAiYmYElAJqE/958uRJBYdZLKDobFZ/P9VnUUAVDPLHIAiIAmqQgdRjN/bu3UuoaKLFf6IPRzkGNMzEGfDAQLOAIgNWI9zGehFBwIwIIAkpLDZpAhLCdCItL2pGxiSYk5Ag2fwTP0UBNfJom69vooCab8y9pseI/4TYWkCPiAWUQiwPVl8fHzprScrymkGThggCHkbgCLvgI5JZQKM4AQmlKo0ugdkDErvIcwFEXkgT4ZC/xkDA+L9gY4yTIXsBBTSSHyR5bR4uxzgGVFzwiTGgeMCelsxXQ9770inHEUjugj/FLvgYE2TAA6FgjgGF3LEooGIB+ATZ7wAAQABJREFUVXDIH4MgIAqoQQZSj91Yu3YtWz8T6ZfQfpSfvMCcd+EmzoAHDkjACgoK4qUsdPbcOUXMj/UigoDZEEAd9EscppPcAhoTYfwEJIx1kKXS07WEBAoMDhYLqNl+AAbvryigBh9gb+7e2rVrqKKlAhLaiQQkiNktoMAghC08KEt6584dusCUMyKCgBkROHbsmOp2WGys+kRGOLwCscyUYQaBFwQlR1ENKYRfzMUFb4ZRN08fRQE1z1h7VU8PHjyoahtXLFbU2q7jllrHZreAApBgVkBv3LqpsBG3m/UWkQWTIQD3O0SzgEIBQ6lejTfYDHCgGtJlVEMSBdQMw22qPooCaqrh9p7Owv0OqVj0ngJ6lEnoIWIBTVRAE67fUHiIAqpgkD8mRECzgIZbLKAowQmJMYkFFH0N5kQkrR68RkGF9SKCgN4REAVU7yOo0/avWbOGIjjbu0DOnNYewAIKepVQEz1crJ1PtgAqpktc+QSiPXST7SJfBQHDIwAFFAT0IRZqthMnTqg5ItokSUgYYFAxwQIaEhlFp4QVw/D3vJk6KAqomUbbi/q6ZvVqtn4WSdIixICiCpJW8STJRpN9CWEczvNDByJxXyYbfOmuFQFrFSQL5RIsgFA+zUDBpIEQxBbQS5cusRIeKS+jGijyaQgERAE1xDDqrxNr2AVfycb9jh7AAirxn4ljiSSks5z9Cy5QUUD1d39Li12DACygmvsdZzxx/Lip4j/RZ2TCJ1pAOQnJEoKA9SKCgN4REAVU7yOow/bv27dP1TivXLxYktYjBjTU5FWQNECggN64eVNVexEXvIaKfJoNAVhAQ2MSM+DRd7jgzZIBr411MCugiVnwUZTAdExYFhEEjICAKKBGGEWd9WE1u98hlYolV0DFAqoNJVzwkPCQELGAaqDIp+kQOAoLaFyiAnqTX8jAixsXGWEqHBADCsUzOCKx3/JCaqrhN3RnRQE19PB6Z+eggMYypUgeS2ar1srjiAEVC6iCQ0vEQiUUyXzV7hD5NBsCti547XdgJgomjHdwQKAadp/AxE8NB7PdC9Jf4yEgCqjxxtTre7Rq1Sqqksz6efPWLRXzKDGgicOnKaCB/n50kt2OIoKA2RC4fv06nWeLpxYDepzjPyHms4Am1oPP6u+v+i8KqIJB/hgAAVFADTCIeuoCKpmAA7RKieJJmn2CHzSQMIubKclGE35BDCjE18dXxb2ZEALpsskR0BROTQFF/KcfJ+WFc0lKM0lwYKICetfXR3VbFFAzjb6x+yoKqLHH1+t6t3XrVkJ95yrFkyqgcL9DhIQ+ccj82NoRwMkH4EU9KZmviaDIX1MhcE8BjVP9xneE7mThf2YSJCFBrt+8Rf7shociLiIIGAEBUUCNMIo66gPc75DkGfDHLGU4w/gBI5KIAKzBt7kWPBR2/BcRBMyEgFYFKSwmRnUbCmgOE3pIEAcOARVTeHS0KKAKDfljBAREATXCKOqoD1BAi+TJQxGc3W0rmgVUXPD3UAnhh+1NSz14sXrcw0WWzIGAPQU0zoQvqFmZhD+QraAgow8VBdQcN79JeikKqEkG2lu6uXLFCqqajP8TbTt29iwFs1Lq6+fnLU3N9HaEsgJ6JeG6aofmjsz0RkkDBAEPIQAFNJQVTh9fX7pw4QIhKSmHCRVQwB1qUUBDWAGVucBDN6Bcxu0IiALqdojlAhoCcCFt3baNqpYooa2yfh5nBVSsn1Y41ALwOH8lsRynPHSSYiPfjI+ALQWTZg01Wwa8NsrBHPsJC2g4k/Ifs7ABaNvkUxDQKwKigOp15HTY7jVr1tAdjmmsVrLkfa1P5ACV+E9bYKCAnjl/Qa3SHsC222VZEDAyAnjpCo2+F/+JvppVAQ2xWEARDysvo0a+683VN1FAzTXemdrblStXkj+72MsUKnhfO45yElJYZNR96828AglZlzj5KDIsVB46Zr4RTNr3I1yGM8xSrAIvYFFMTeabLZGKyGyQgIrp0sWLjEcMXTh/nm7cuGE2CKS/BkRAFFADDqq3dgkKaMWiRZnb8v6HiHLBmzS+K6Xx0hgBYsLCCTWxRQQBMyGgXPCWMpzHWQE1a/wnxjwULvjLl8iWEcBM94L01ZgIiAJqzHH1yl6t+Ocfdr/fH/959+5dOikxoPeNmaaAhgUF0dEjR+7bLisEAaMigFCdUydPWqsgQRk1swIawgro1avXKDQqWg25hOQY9c43V79EATXXeGdab/fv368I1e0poGfYtXSLKyQJCX3S4dHKkgYFZKfDhw8n3SjfBAEDI3D69GlC1TRY/K5du0YXeI7IEWXeGHEooJBslk9RQA1885uoa6KAmmiwM7OrcL9D7CUgHdOqIIkLPskQIQkJlZAQ9yYu+CTQyBeDI6ApWOGxcaQt54oyb4x4aGCQGvEslnrwMh8Y/Adgku6JAmqSgc7sbq5g/s/cHEAfb6lqYtueE+x+hwgNky0qRFmzZaOw8HC18gxjlJCQkHQH+SYIGBQBTelEHXht2dQu+KBEC+gVTkoMY0VcFFCD3vgm65YooCYb8MzqLgjoqxW/P/4T7bGW4TSxhSOlcYlg4umbt26pzUckDjQlmGS9wRDQqIbggocCGhYcTAEW65/BuupQd5CEBAEXaERcDpK5wCHYZCcvR0AUUC8fICM0DxVM1m/YYDcBCf0DB6gf0zMFcrKNSFIEwqKj6DLHwEEOHTqUdKN8EwQMigCUTn9WugJY8UQCXk4Tx39iiLPz/Aj2EFSECssRJzHhBr3vzdYtUUDNNuKZ0N9169bRzZs37cZ/ojkowxkh8Z92RyaCs17PsNUDIgqoXYhkpQERgAIaYeEAhbs5p3AEUyi/oEMBjcyRkw6LN8SAd735uiQKqPnG3OM9RvynD7+9VyxW1O61YQENFfe7XWwiUPmE8QniSiiigNqFSFYaEAHldmcFFHHP55h4PSd7AswuoRwHepHZACJy5BALqNlvBoP0XxRQgwykN3cDGfDlChdWbiR77YQFNJQzvkXuRyCSY0Cv8kM4nh/GoLISEQTMgICqgsR1z7VkGzNnwGvjHcaZ8LCARuXKSZfZK4J4UBFBQM8IiAKq59HTSdsVAX2J4im2FklIGudlijuZdAOSkCDRoSGigJr0HjBjt2EBRQa8poCKBZQohC2gKMMZkTOnuiWEG9iMvwxj9VkUUGONp9f1Bg8QxCtVL1UqxbbBxQxqEZH7EYi0xMEh/mv/vn337yBrBAEDInDi+HEK5zKcmD8iQkIowM/fgL10rkvhQcF0HhbQnLnUgQcPHnTuBLK3IOBlCIgC6mUDYrTmIP4TYq8CEtYjwxsuZrGAAo37JcrCm5rd148O8AMHZUtFBAEjIwA3M6ofwQIKuqFcFi+AkfvsSN9ARXXjxg0KtCRsSky4I6jJPt6MgCig3jw6Bmgb4j9jOL6zgMVtlLxLR7nkHkQU0OTIJH73ZfqV0LAwoiykHj7C/2cfJ1lrHATgfockKqCHSeI/E8c2PDiRpi6BldAQnlPFApqIi/zVLwKigOp37HTRckVAX8I+AT06cJzjPyFSB17BYPdPdFwcXWMuVciePXvs7iMrBQGjIKApoL7scr506TLlikmMgzZK/9LbD1hAIec5DjQ6d246cOBAek8lxwkCXoGAKKBeMQzGbMQtruCzZu3aFN3v6PWxs2dU58OEBzTFmyCSFdCz/CCG7N69O8X9ZIMgYAQENAX0iqUCmLjgE0c13KKAnjt3jqLi42mfxIQb4XY3dR9EATX18Lu38//995/i8Usp/hNXhwU0G9c8D4GbWcQuArCAHjx5gnJwotaOHTvs7iMrBQGjIAAF1MfXl84y52WWLFmYhD7SKF3LUD+CsmdXfMrKAhqfh/YJLVuG8JSDMx8BH0814TTH+q1la1j+/PmpWLFiKV4WD1i4FipWrEjRNsHn69evJ5R01KRo0aIUKROTBodXfiL+M2vWrFS5eMoUTIgBRfwnHjQi9hGIZuLpE6yo1yxTmnbu3Gl/J1krCBgEAZX5HhunEpBycAlOH35BFUlEIIKtoLCAxuTJQ0cOH1YV5nxZWRcRBPSIgEcsoFAeu3Xrph6egwcPppkzZ9rFaty4cfTOO+8Q9u/Ro4c1yBqu3BdffFEdh2PxX5Ix7ELoVSuRAV+6YAEK5io+KclxJqGXBKSU0ElcH8MKKLLfc3FZzm1btqS+s2wVBHSOABTQMM6AP3zoEOW2MULovFsuaT7c8LCAQgHFnCCJSC6BVU6SSQh4xAI6fvx4Gj16NJUrV47atWtHPXv2pObNm5MfZ/hqgiovS5cupRkzZiir2XfffUfTpk2joUOHKgLueI55efvtt7Xd5VMHCICAvkHplPk/0YWjp6UMZ1pDGWNhEAgPCabde/cqT4C/v39ah8l2QUCXCGgK6E52xVesUV2XfXBXoyN4DsBLe0zevOoSe3k+KFSokLsuJ+cVBNyKgNsVUFgvUbGhbNmyqiNxHM8WGBioLJgFChSwdg4/JOwDly0ELvi5c+eq5V27dhEU0Pnz56uHb+PGjdU51EbLn6+++oq2bt2qvok71xaZzFk+deoU7eUg+aHt2qbagCNnTlPePBVS3cfsGxEDinvaz8eH7ty5o+JAtd+T2bGR/hsPAZThjC1fnm5dvkLxFh5c4/UyfT2KCAmlrYcOKwsozgBWDDwPRQQBPSLgdhf8yZMnKYiruNgqhWGccHKW3+JsBYHnWK9JaGgoneEKORDEvSE2FLVv8QkrKmJKbeU4V86AEov/Qk9hi0zmLCP+E1K9ZOoWUCQhSQZ86mMELlAQ0l+/eVPtuHHjxtQPkK2CgI4ROMYK6F1LXKMooEkHEhbQy5cvUxaOi41iz4iwYiTFR77pCwG3W0CR4Xz79u0kqMAqmp0z+mwl+X7YJ8ASO9irVy/Cf1hOIUhGgjW0c+fO1lMMGTLEunyRsydLpVL60bqjLLgNAcR/RvBLRNE88Sle4wpXO7l89apwgKaI0L0NMblyqXAFZMKDXcD23r+3lywJAvpGAHP3VZ4TEu7cJZSfxX+RewhE8pwKQRxobL58ooDeg0aWdIiA2y2gUfzAvHLlSpIMdlg/c/ED1VZi2MJjaxXFck5L7BsSjmwz4PNy/IvGFWd7Dln2HgRAQF+Vs99tLd/JW3fUYuGOkDrwyaG573scE0/vOHyIynG81/p16+7bLisEASMggPhPyCU2MuThRCSRpAhEhISoFfAOxnEI23ahZUsKkHzTFQJuV0B9OG6tWrVqNHv2bAXMkiVLKILLiOE/BMlHCVwLvEqVKrR582ZCfVtYP3/99VeqWrWq9ZgJEyaoZbwdL1q0iOrXr6++yx/vQwAW71X//kvVS5VMtXHWMpyS6ZoqTtiYg2Og9x89RuULF1Lk/lITPk3IZAcdIqApoKc43CpPbIwOe+DeJkdZLKBQQHMUKEh7OQY0uYfRvS2QswsCrkPA7Qoomtq3b1+V3d6xY0eaOHGiymzXutCnTx8V14mYz969e6sM+SeeeELFe2J/yGOPPabiXkDlhPhPJCjhv4h3IoAYRbwo1EgjDAIZ8BCxgKY9jjmYduUWK/bxMbF04cIFcb2lDZnsoUMENAX0Brc9b5xYQJMPYXaOBw/k8DUooDnZG3KT48JhxBERBPSIgNtjQAFKPo5V+f7771XcSnh4eBKc5s2bZ/3eokULevDBB5W7PdhSdgwboZy+8cYbSqlBrKhQ0Fgh88qFf5h+CWwGVUqkTECPhh/jSRQkysGWt3qv7IyXNConK6CQoIDE2GnE2BYpUsRLWifNEARcgwDCrXxYycrC80LeuByuOanBzoI4UISoVS1TRvVs+/btQsVksDE2S3c8YgHVwEyufGrrbT+VQmKjfNpuQxKSKJ+2iHjnMhRQENCHWJLGUmrlkVOnxfqZEjjJ1oOKCb8NKO0FOR50+fLlyfaQr4KA/hGABdSf4xxRvCLSEu+o/165tgfRrICC5i6WcyHAkLFt2zbXXkDOJgh4CAGPKqAe6pNcJpMRWM4FBdJyv6OJqgynJCA5NFqwKMMKuo3L1NYtV5b+/OMPh46TnQQBPSGgKtz5+1M+rv4lYh+BKKYrPM0KaFb2BuYsWNDKf21/b1krCHgvAqKAeu/Y6LJleIAc4ESyB0qXTrP9h0+fonBJQEoTJ22HXPnz02aO96rP8c97mO9WYr80ZOTTKAgc4qIlN7joQv4ccUbpksv7ERMeRuc5Dhzxn7mKFqWNmza5/BpyQkHAEwiIAuoJlE10Dc017IgCeoSLCUSIAurw3RHPCuj2/QeofoXyit7KNn7a4ZPIjoKAFyOAl6q7vn5iAU1ljKLDEvMoUIwlvlgx2rpli6qQlsohskkQ8EoERAH1ymHRb6OWLVtGuZk+JV8aFgzQCB3nLPhwccE7PNjx7G5LuHGDznEllBqlS9GsmTMdPlZ2FAS8HQGUmYVrOQtneRewcEB7e5szo30xEYkVA1FlME/xEnSNC3qgJKeIIKA3BEQB1duIeXl7lyz5m2o54H4/ee6cohWKlFrPDo9oXqZdgWzcvYcerlWLFi1ebC1X6/BJZEdBwEsRQDllKKFh/FIalKxSnpc2OVOaFcX14LNxTDgU0HwWqrsNGzZkSlvkooJARhAQBTQj6MmxSRAAP+XGjZuoVtmySdbb+4IMeIi44O2hY38d+FJDOQFhw+7d1LZ+PUVA/cMPP9jfWdYKAjpDQCUgcZvz5U2kHNNZ8z3WXCQkRjOd4YkTJyiSLcUhXNRl/fr1Hru+XEgQcBUCooC6Ckk5Dy3l7He41h1RQA+zqw0iFlDnbpy8hQvTuh07mZA+hhpWqqQKOzh3BtlbEPBOBLZwLCOkaGHht01rhOIiwgkWY0g+9jitXbs2rUNkuyDgdQiIAup1Q6LfBv3999/qzbxk/nxpduLwqZMqkUZiQNOEKskO+Tnrde3OnUrRf6p1S4LrDeVtRQQBvSOA+HFImZIl9N4Vt7c/LiLSqoDmL1OWVq9Z4/ZrygUEAVcjIAqoqxE18fkWLVpEddj9noVpVNKSw+yCD2fXESpbiTiOQMHixekCJyHtYrqalg88QEWYG/SN0aMdP4HsKQh4KQJ4mcrKHKA5JTExzRHKERWpylVfuXKFCpUvT+e4MpIkIqUJm+zgZQiIAuplA6LX5pw/f17FIdVjiiBH5DAH0Iv73RGkku5TiBVQyKqt21S506GdO9HC338nWJ9FBAE9I7CbY5uDkpVq1nN/3Nl2TUk/duyYUkBxLZTnFREE9ISAKKB6Gi0vbutff/2l3MIgSXdEDp5kEnrJgHcEqiT7hLLVOJarxKywxMt1bNSQyhQqSIMGDhQuwCRIyRc9IQBOSyQxRksFJIeGLYfFSozSpeFcpjcmPp5QAllEENATAqKA6mm0vLitv7MVDvyfxRzMYD0kFtB0j2ZhTjpYZql+gozYsf2eoXWcBfvpp5+m+5xyoCCQmQj8+eefxLE7lMfB+SMz2+oN187u60soyakxBxSuXJmWcBKoiCCgJwREAdXTaHlxW39fsIAaOWj9BNffMbZ4RMXGenGPvLdpRcuUURWRTrPFCFKXY8A6N2lMQ4cOpcMcGyoiCOgNgfnz5xO7UCg+XiiYHB273NFR1t978WrVaMvmzXSWY0FFBAG9ICAKqF5GyovbuZfrku/iShyNqlR2qJXHzpxRJPSigDoE1307lShXTq37a/098ul3+vYhf07o6tunz337ywpBwJsRAHXb3LlzVBNlTnB8pHJHx1gtoCVqPKAORCiUiCCgFwREAdXLSHlxO1GTHJnvjdkN5IjA/Q6J4tglEecRiM2Vi2IYu0U23H9wx8EV/+ucOSTk9M5jKkdkHgJrmELolKUwRaR4RRweiDxxMaoMJ+JncxQoQFE8L/zxxx8OHy87CgKZjYAooJk9Aga4/pxff1W1ySNDQx3qzQGu4AGJkiQkh/Cyt1MJDndYsDop91+Hhg3owapVqX///nTx4kV7h8k6QcDrEJg1axZlZ/olSLS8lDo8PnliE1/gDx48qI4pVbs2zUMog4ggoBMERAHVyUB5azMvMyclapI3q17d4SYePH6C/Pz8CBndIulDoGyVKnSIFfltBw4kOcEHA/rTBabEGjFiRJL18kUQ8FYEZvz4AxXJnZv8uf57sIMvsd7aF0+2K4qxCg4IoH379qnLlq1bj/bz8o4dOzzZDLp58yb9+OOP9NNPP9GNGzc8em25mL4REAVU3+OX6a1H8gAmnZY1E2OQHGnQflZAo8XV5ghUKe5Tistw+vj40Nx/knL/FeDa0C92aE8fffQRbdu2LcXjZYMg4A0IrFu3jnbu2k1xkREyJ6RjQPLnzEGIwYeUqVOHfDg7/lf2SHlK4P6vwi/D7dq1o7Zt21INNkRIIpSn0Nf/dUQB1f8YZmoP8NZbLF9eKpEv7fKbWkMPsuUuUlxtGhzp+szOlo8SnP0+c2li+ULbk7zweAdVTeaF55+3XS3LgoDXITB16lQKCw5GArzMCekYnYL8wrl//366ffs2BYSEUIkaNejnmTPTcSbnDwGbyaNt2tCh/ftoyUcf0F8fvE97uZhA506dnD+ZHGFKBEQBNeWwu6bT165dU2/bbTj2yBnZy9U7hHDaGcTs71uJcV/NVs7Dp04l2SGA4+lGdu9Gv3FymGTFJoFGvngRAnDdQgFtV78+7eeX0hghoXd6dApx6MKtW7fogCUUp9JDD9EKJqQHQb275fPPP1fco9+8/DJVL1WKHihTmj59fqCKQ0Vcr4ggkBYCooCmhZBsTxGBOZxxjVrEjzWon+I+yTeAcgWxi6KAJkfG+e+VatZU5ThnLP7rvoM7NW5EpQsWVNyg922UFYKAFyAwY8YMzn4/RT1aNEucE8Qr4vSowAXvw/RrO3fuVMdWfqipmhMQk+lOuXr1qoozb1uvHjW2od97tG5dTkgtTa+9+qo7Ly/nNggCooAaZCAzoxtTv/lGKTmlmQLEUTnORMkJHDMq1g5HEUt5PyRslK5ciab9wVVkkgkqJMEKunLlSsKLgogg4G0IjB07VrFnxHEy4g22hspLqfMj5JvNhxD3vWP7dnVwKJfoLFWrFn3DlmV3ysSJE9XLw2vdu953mSGdOtJ/GzfSokWL7tsmKwQBWwREAbVFQ5YdRuAkc3nO/e03eoIr8Dgj+44eU7vH8KQpknEEajRsRP/t2kVb9u2/72RIDKtcoji9Khnx92EjKzIXAfBVgv9zUPt2tI9DciAyJ6RvTEpwDP5OngMQ0gCp2eZRWsvYuisJES7/se+9R7B2Fs1zf+Wqh6pVJYQGfPrJJ+nrkBxlGgREATXNULu2o5MnT1bk852cVEAR/wkRC6hrxqPiAw9QYFAQTUmB/++1rl1VnXiJyXIN3nIW1yAwnOMGyxQqSK04jGSv5aU0Vl5K0wVuyfz5VRyoRr+EONBATuzCHO0OwVxyiEv+Dnisrd3ToyhJt2ZN6ZfZs+kMV70TEQRSQkAU0JSQkfUpIoDsxwmffkoPs6sn1kkuz70cHB/ECpPw/aUIr1Mb/DjhqBoncXyzYKFyYyY/uEnVKipBADFZiL8VEQQyG4Gvv/6aVv37L73Rq6d6icWcEMwZ3FCaRJxHIE9cLIXynLqR3d4Qf2bIqNaqNU3+6iurVdT5s6Z8xMcff0wVixWjqiVLpLhTR45Bh0UWcb4igkBKCIgCmhIysj5FBMAzt5+zLvs83DrFfVLasPfIUUIpSRHXIVCvWTM6c+ECUzIttXvSV7t1UTFZP//8s93tslIQ8BQCR44coQEDBlBrfnl9qFo1dVkooGL9TP8IZKEsVK5QIVq/fh3BOACp1/FxOs0JXq72fCDZCcwaT7VqmWqD47nKXa1yZen7775LdT/ZaG4ERAE19/inq/fv/O9//AZclGrzBOOs7OIHUIwooM7Clur+eQsXpkLFi9OEX2bb3a8hk9bXLFtGxYJqDyi7O8pKQcCNCIC2DbyRvlmIPhr4nPVKu/ilVOYEKxzpWqjEFsmLFy9Zs+ELlClLBcuWpY/YWulKAfVSCFtb2zvAfNK2bh36e8kSlazkyjbIuYyDgCigxhlLj/QEb7/LmWfuhQ4d0nW9PayAigU0XdClelC9Fi1o+cZNtNlSli/5zq93705btm6l6dOnJ98k3wUBtyOA6jjN2VK/YcMG+uG117jyUaT1mrs5njCWk1ZE0o9AkTy5KYLDGP7huVmTBk88SUv+/pu28u/eFYKKd1+xW79DgwYUyGVT0xJYufHC+8svv6S1q2w3KQKigJp04NPbbSQPlGLapTZ1nCOfx/XOXbpEZy9epBzx8em9vByXAgLV6tWjEKZl+nSm/cke1urGXDLvleHDpV5zChjKatcjcP78eQJlTxnmhly7ZjXNenO0IizXroTQkQuXL1MOUUA1SNL1CTd8jdKlaO3atYqbGSep3qoVBYeHE2I2XSFQJFF6E7ytjkhudsODhcPVYQCOXFv20QcCPvpoprTSGxCYySXeYP2cMep1RXbsbJt2HTqsDhEF1Fnk0t7f18+PanP261TOPH2zd09V3jD5UW/w+mq9n1Z14gcNGpR8s3wXBFJEANQ7IB9H4QlYM48fP66q76ACz6FDh5Sb9SK/XCoqIE52g7UMGdAHDh5UyW8tmBJsbL9nKH+yakc7ZU5IEXNnN9TkSkTzVq6ipRwL/hDPBX5spazTvj1N4aSvMWPGUAhbSDMiEz/7jCoULUIVixZ1+DQtuTTom9Omq3snMDDQ4eNkR3MgIBZQc4xzhnuJ+K3nWWmpU74ctapVM13n28EPKogooOmCL82D6rdsSdf5wY+MeHtSnmNFOzNt1uuvv64sGfb2kXWCABTHT5nlog3Haxbm5BYoDr6+vhQWFka5OH67NFszGzVqRD169KAPx4+ndcuWUgIrpNEMXd6A7JQvKJBKxkRTy8qV6aMBz9GO6VPp59Gj7lM+gbR1TrDDJykj4RwCEcEhVJljQcGxihcASMPOT9BVfmmYMmWKcydLtveePXvojz//pJ7NmyfbkvrX5g/UoOvXr9Pvv/+e+o6y1ZQIiAXUlMPufKdfeeUVxf3284hXnD/YcsQOtoaE8kMsKINv4ulugMEPjOZShuU4s/gTdpU90+YRRXGTvMujmfrm5yVLadiwYco1mny7fDcvAufOnaORI0fShAkTlAJTpUQJaszJa3kaNmCLehAF+GenILaqIdYwLjKC8vL9FsyUPxmRnfxSGs5UbuCyFck4Ak2rV6M1U76mP1lZbNq0KcXkzUvlOGYTyUj9+vVL9wVwTwTzi0iHRg2dOkdZfoHJw/fJ3LlzqXVr51lTnLqY7Kw7BEQB1d2Qeb7BixcvJpTNe6XLkxz/mT/dDdh+4CDl5AlRxH0INGAr6HusXC5at46Q/Z5ccnKpvhE8ji9N+Iy6dOlCNZkIXEQQQJxe79696TLHaT/3aBvq+8jDlCsaNk33ynZ24ecQ66fLQM7ByV1wxf/GVeqqV69OEazcN3qyC73Lv3nM4/WZM9hZgffryy+/pE7M7RmSDjd6M1aKZzN1n4ggkBwBccEnR0S+J0EAvH2PP/44VStVkoZ07pRkm7Nfth48IAqos6A5uX8pVjrj2E36WQqUTDjds20fpfJFilBPdqEmJCQ4eQXZ3UgIIGbz2WefpUceeYTKc0WdTV99SbCSe0L5BI5b5aXU5bcTqkv5ZstG06ZNU+cuwyUz4/Llo0/SWRpzKteVR9wvXkrSI81YET7GIRrr169Pz+FyjIEREAXUwIOb0a5d4AzVZkydwpkF9C273n14UkuvIDYRJPS5eSIUcR8CKIMHSqZfl/9Dx1Iog4dxnDj4BdrNcV1Dhw51X2PkzF6NwCkmKm/AFjHEe7799FM05+23lFvdU41O4DlhH5PQy5zgWsQRFgGezk2bNimrp5oTHu+ostFPnDjh1MVQPe09rvuOimol0jl316tQnrJzxTa44UUEAVsERAG1RUOWrQggHuzBJk1oHyspv7z1BoFSIyOy/eAhxQmXK52TWEaubbZjazZuTFlZyfzqt3kpdh0JSXDFj+ckErjrRMyFwPbt26kKJwlt3byJ5r/7PxrYvp3dmGF3orKN3e9QcHKz5VXEtQggGekBThb74YcfFDl97XbtiAdYudKdudJPP/1EqDH/Qof2zhyWZN8AVj7rsxI6d86cJOvliyAgCqjcA/chsHfvXqrFbpxtW7fQXLaKOEO7cd/JLCs279urluKZQ1TEvQiEcKJXBR6/SayAplb/fTCX66tfsSI98cQTilLHva2Ss3sLAqtWraIHHniA/Fj5W87JKXXLl8+Upm3eu09dV+YE98DfvmF9xTwA1/tlzkSv2ORB+vyLL1KdE2xbcvv2bVU9DVXU6lWoYLvJ6WW44Vf9+6+wbziNnLEPEAXU2OPrdO/m8FtqJY4jvHzuLC1+fzxVL1XK6XPYO2DTnr0q2zWUiZFF3I9AXc6APchxV0hGSkmyZs1K3wwfRgFsLUWGKjgeRYyNwIoVKxSFUoHYWPr7w/epUO5cmdbhjexdieCkuGAuoCDiegR8s/nQ061bURi75JFEWo65QfexcQHJSI7IF6ysbt22jUb37OHI7qnug+x8vAyLtyVVmEy3URRQ0w25/Q4jGeGFF16glpxFXalwIVo54VMqU7Cg/Z3Tsfa/3Xso3oXnS0cTTHVICbZqxTLp9+S5KbvhAUgsZ8n+xIUFdrBLtgOXV4XVQ8SYCCAmENQ8JTjrfOF771A0W8ozU6CAypzg3hFAPOhzj7WlAB8fmslllCN4Tpg0aVKaF0WsKOLD29arx1n1ZdLcP60dQNlVmud/GDhEBAENAVFANSRM/Hns2DGqx5mS48aNo5Hdu7HbfYzLH07rd+2ivBx3KOIZBJB4UPPBB2nWsmWqBGpqV61YrCh9/fJQlSTQp0+f1HaVbTpFACUUWzCJeK6IcJVsFOoFvJvrd+2mfMwTKeJeBMJ4rAe2e0xZQq+FhdOPP/5IKJGakuAl9EkOy+G3UXqvX9+UdnN6ffMa1Wn+/PmJ1bKcPloOMCICooAacVSd6NN///1HlStXol1sAVv43rs09InO6SqzmdolD508qWrA52fqHxHPIYBkpJtcQvH7PxeledGHa9emDwf0p88//5wGDhyY5v6yg34QgOuzc6dOdJGVjplvjFZE8pnd+v0cHnKeOUfzyZzgkaGAEjqIE80Kcsw3vF2I+7bn7cC9gpfQ37ma0uQhLxF4g10lLTnu+BKP+V9siRURBICAKKAmvg9QM7g2Kx6R2QNoxacfqzKb7oBjzfYd6rSigLoD3ZTPGcVxfqX4gTN53vyUd7LZ0rtVKxrDdDzIjO/fv7/DyQo2p5BFL0TgY040WrBwIX0x+EUqyByx3iBrObMakt+JuuLe0G49tyGQs9EHMfNFNLMOwBVekROLkOWucQFv2LBBMZ/gJRQvo83YYulKqVKiOFfQiqRfuFKbiCAABEQBNfB9gKSSy5cv2+3hMnbNPsRB6aXy5eVko3Fu5f/7lwPZQzjRICZnTrttkZXuQ6AmU2mt37mTNu9LzDhO60qwkrz7TB/68MMPlZVEqymd1nGy3TsROHz4MA0ZMoSe5HCMVrVq0hWuanOZ/2e2/Lt1G4VxQiLKx4p4DoFsnHjYpn0ipdIdfj60bduWgtg6iv8VWCHd8t8G+vmNUYSXUVcLwoJacm34WTNnysutq8HV6flEAdXpwKXW7K+++opKcvZ6cHAwhXDd5jxc/nLAgAG0i+MwIWvXrlUE82ULFqDf/vc213kOTu10Gd62YstWKsh1pUU8j0AlpmNCne0pDlpB0cL+/FD64qXB9P3331Ojhg0JhOUi+kTgpZdeIn+fbFShaGEq07U7RTRrQZH8v2CHx+n5jz6hXaygZoZgTiggc0JmQE+VatWiAC6p2aRKZVo1cQKN5TjPYZ060gwkI079hlqwq9xdglCfI1x84F+mZBIRBEQBNdg9gPre3bp1Iz/Oduw+5m3q+c67VJh/9JNYKS3G5MRt2rShxhwbWJAtD7++9SYhS9KdcoPjjeBuK1yypDsvI+dOAQFfPz+qWq8eTfv9D7rlRIb7kw89yC8nY2jLpo3KMgLuSBF9IYAXzenTp1NB9jwM/PBjys5zQjeO7+02aBAVqlSZvlywgMp06Ua93v5filWz3NFjVEVbx1Z5mRPcgW7a5/RjV3wVTjqduvB3xXTSl8uwghMYFnJ/ni/cKfUrVlAGD7j+RQQBUUANdg98++231LRXL3p+8ldUj2u41+EKGN3fGkPjVv1LrfnBM4tjf5AB2a5+PfJEJiweNHjgFOGqHCKZg0Btdr+e5jH/bcVKpxoA8mnQccUGBVIttpp88MEHTh0vO2cuAsNffpkK5s7Nyt4ueuixx2jAqFFUh2mY6nDoTVf2iLzHtcLbdu9OM5Yuo1JPdqWJs2d7pMFrd+wkvJgWdRHHsEcabbCLYE44ydXu5nv4xdKX6aBa1XyAZnCFJhFBQBRQA94D4Xbiqu5y/M22i5cosnETqsAW0Zc//4JaDhlGp7neuzvlr/UbyI/fqguy9VUkcxAoWLw4xXMJ1C/TUXIzP1vNlnz4AXVt+hA999xz1KljR7rmBTGEmYOkfq4KF+d8tnAO69xJNRqE78nFn70fTfkF9c3Jk6lMjRrUb9z7PCcMpTPunhM42cWfrXAFZE5IPiQe+w7rcy4OzfrqN8cSFF3ZMHCL7uMyrOKGdyWq+jyXKKD6HDenWo1Eko8++oiOM/XJs493oGeHD6d+I0bQ8q1bqdpTTzucoOLURS07/7F2HRVmSwdcwSKZh0AttnotYCv4sTNnnG4E3HKfDBqo4kJnzJihOGMlLtRpGD16wJi33qJ8/PLQsXGjNK+L6mS9OVb0GZ4Tlm3eQlWf6kNb9u1P87j07vDHmrXKI+Lj65veU8hxLkCgFltB53JlrONnz7rgbI6fohHT/kVyEQSEh4iYGwFRQA02/slrf4Pz7VOuBYwSbH243GI+i3UUgegjWCm9zZaIes8+Ryv4weNqQcbtis2bqTSX9hTJXAQeaNSIsnAGrDPJSMlbjLjQP8a9R3s4rKI23z9HOZlAxPsQ2L17N81iqpuB7dqSD5dZdVQq85i+gjmBXzjqPtufVm5x/ZyADHyct5TMCY4Oi9v2A08wsWfs6/kL3HYNeyeGG75t3TqEcLFbzFMsYl4ERAE12NiD6kKTq1ev0vvvv087WWHo3aolFcubR9ukPmOZE3DYuPEUy66Ypi8OdvkD50+2foIIvUyVKkmuK188j0AIWxwqsoLx+Zy5dOfOnXQ3oDpbsxcxbdd5rqzTsEEDyZBPN5LuOxC/eTBbdGGrt7MSxzGjQ7kiWnR8PM8JLxHoklwpv69eo5LhZE5wJarpOxcs3xWZJQNzQnLDRfrO6PhRnZs0ppNcoGQBh4mImBcBUUANOvZwt48ZM4YOcqzNM20eodIFCtjtaRDTNA1id10OjhFETKgrXW+/MNco6pHHp3Btuw2SlW5DoH6LFnSIazzPZ1d8RqQE3ysLuJb4yePHqDmXd5SY0Iyg6dpjL168SJM5prN7s6YUlE6Gi2Dm7H2B5444rhnf/KUhtI3nEFfJbJ4T4jgrPzffQyKZjwDmhANcinmBh2mR8CJblA0ik7/8MvNBkBZkGgKigGYa9O678J49e+mNN96g29cT6MUOHagYP0hSE3DCDeT9gyIiVBKCK2KCkOX6y7LlyuqW2rVlm+cQKF62LMVzFZSPmQg6o1KSzzOLyzpu5FKu3bp2zejp5HgXITBlyhSC5+Pph1tn6IwBzB078M03KZCtZC1YCUXGdEYFbBiz/1lBlTgJUsQ7EChRvjzlZg/YxzNnebxBXdlC/wszL8ASKmJOBEQBNdC4/8H1exFTs27dOqpQuBAN5QzYXNH3Z7/a6zKsHgNGj6aL16/To8NHKOoke/s5uu63lSvpIlfaqFa/vqOHyH4eQKAhxwHDDeoKqxasGJ8+P4i+Z0qV9957zwOtl0ukhcAnXHazWfXqBPaCjIqaE/jF9Py1BGrDcwJeKjMic5kG7BLmhHr1MnIaOdbFCDR8+GGVoLjj4CEXnzn10yGmHPKlWEFTB8rAW0UB1fngIhEEGe7l+U0WBPOQ2uXKqPivAD9/p3qHUpnPvPoqreeKSc+MHe/Uscl3njT3N2Vtk/rvyZHJ3O9IRgrleNBx37uGhw+xXH34AYaKO8uXL8/czpn86osXL6btXPTh6YddV0Yx1jInoJhEP44Xz4h8yXNCHg7HyVu4cEZOI8e6GAHMCaiYN97D3Jyx7HFrw9ZwvDTddqJIhou7L6fLRAREAc1E8J299AmO3/vzzz8VIXiPHj2oJJeyy81JA/3796cYpjT5afQolfUaGx7h7Kmt+xdlwvjO/frR1xwc/uGM9FWr2H3kCC38dzXV4/giEe9CAFVQYAVFZaRDLnJ9vdP3aSpfpDB16NCeznqY0sW70M3c1oDtogAnFjZxcdJfsTJlqNMzz9BXXM7145/TF76x89AhWrh6NdVv2TJzQZKr34eAf/bs1IBfIr9esJAOe7jsLvITDnE52FmzPB8CcB8QssLjCIgC6nHIHb/gdXaH/8BvpR2Z/DsXWyJysFutEb+tDuRyeisWLaLK+fLSxMEv0sEZP9Bv77xNLbnChCukbrNmhOD0Fz+dQH+sWeP0Kd+d/h0FcRZuLYtF1ukTyAFuRaARP2x8WRF9a+o0l1zHj19+po94hS5ytSWUghXxPAJIOpzJD/FeLZozs04WlzcA80E9Tjh74eNPaBGzWzgr7377nbKywdom4n0INOY5wYfpt96e5lluzhqlS1HlEsUlhMf7bgmPtEgUUI/A7NxFEhIS6O2336Z4pkJp3749bWCy4EdZuZzy8jBaO+lzujBvLv03eRJNGvKSqlATFxnp3AUc2BsWD1g+2r/2ulNE9dsPHqQp8+dTk7ZtCZVWRLwPgUB+OXjw0UdpMrtEYZlyhRTgF6QJzz9Pc7jU6zim8RHxLAKTJk2irKx4duXsd3cJ5gSU1G336mu0df9+hy+DfWFde5DLgcLaJuJ9CIANpQnPCZOYkgkeLE/KIK7GtYKfccuYIUHEXAiIAupl4w0XeykukzZs2DBqxmTNa76YqJTNsf2eoccbNaQyBQsSKtOkJK7ic8vGBNaojBISFUnNB79E+5iqIy0Bv2Tf98ZROJf9a/LII2ntLtszEQEooKEcg/U8W7RcJW3r1VV8s4MHD1YPFFedV86TOgKIn5sw4VN6lMm9ozm+113iwwTimBOC+YW3GXOE7mera1qi5gSOJ4+MjqbGMiekBVembn+IjQbBfP88/5Hr5gRHOtSmTm0qzMaWNzgJVsRcCIgC6iXjjYpFz7MFCS72WKZFguL5xZDBVLZQIada6Er3m+IIffMtupk1GzV4biDBupmajJryNS3buJG6DBgglo7UgPKCbbBOt+vdW2W//rBoscta9N4zfdU925YfZnALi7gfgdlMZXP48BF6mmN73S3IjAdv8I2sWdSckFbm9GuTv6J/Nm1ScwLij0W8F4HsmBN69aJ5zGDy099/e6yhWblC20udHqf5nHewatUqj11XLpT5CIgCmvljQIfYDVqbswHHjx9PI7t3o78+GJ8icXxazXWVBVS7TlRsLL3EFDsoz1ezbz+yp6zgmm9+M5Xe+PobasnxqlLlREPPuz+rM0VWuWpVqd/4912WkATr/A8jX6UbzEX5CMeVCUm9+++BDz74gCoULUKIp/OEqDnh3ffoFsf+1uz7DM34635lBXPCaH4hHcNxxq07d6bSlSt7omlyjQwiUKNhQyrLSWx92WrtyYSkTpwvUIgTaoe//HIGeyCH6wkBUUAzebQWLlxIFSpUoANcvxl1toc+0ZnwRuhNEs3141/m8n4FkSE/ajTV4TrRn8/+lf5cu5YmzZ2rFNPXvpxMzTiWp42QknvT0KXZlm6DnidiReLRV0bQFa7T7QrJy/fLjNdH0vr166kd3xNS79kVqNo/x39cCOCvv/6iZzzs3o7mhEjMCQVY6e048nWuHf8cffHrHOuc8AC/rL7+1RRqzoUwHn7ySfuNl7VeiUB39sTd5RCstq+8Slc5H8ET4sPXe7VbV/qDQ9DAZy1iDgS8S9MxB+aql3C5Dx06lB588EEqlSeeVk+cQLW4Uo23iiKlHjVKxYCdvn2HnmFOwKYvDKY+746lc9zoQVw15bGePb21+dKuFBAI4zjQfsz9uu3AQWo9bDhddpES+kCZ0jT1lZdp3rx51J6V0BtcBUfE9QiMHTuWkITYoWED1588jTMmzgmj6ZlXXqFTXACj79hx1jnhAh/7PM8Jbbt3T+MsstnbEAjj+6nfa6+p5NPWw1522ZyQVj/bN6ivLPkIRUPssIjxEfAxfhe9r4f/ct3dXqysbd6yhYZ3eZJeZqsnkn5cIa6MAbXXnsq1ahH+X+WKJheY8zGEg9bxIBLRLwKFmE/22ZEj6UN+6CDMApRKpQrkz3CHWvN9AiX0ydFvUsMGDWjGTz9RHFtHRVyDwEGOyZ4+fTq92rULgQors6Qyhw/hv8wJmTUCrr+uNid8xHNCrWee5TlhOKH8rjsFz653+/alhgMG0sSJE+npp5925+Xk3F6AgFhAeRDwtrVz507lyoJLfCUHYR9mclxXx1Pu4GoinTkeqlq1anTr8iVa8uEHNIIfHq5SPj15PwVyreicXGNelE9Pou6+a5WqWJGGsTXtLFsqq/TqTc9yXKgrKJoerVtXcdRu27yZyjCt1/fff+++TpjszGPGjKHA7P6cfOS6ykcZgVDmhIyg533HlmYWlqFMqXaG3fCVe/am/u9/QLv4uehOqV2uLLVnaz68g1Ij3p1Ie8e5TW8BRQJQRX74nj59+r4RCeRs9NJc77ocl7ksy+5xPEBLsLUolhNzHBX8iOCGnDZ1Kv3OsS0x7PJEpvDTD7cmX6Y1cbW4Wml2dfvkfN6LAEokjpwwgeZx8YNvZs6kz36ZTaULFaTG/CBCgks1pgfLyRRbzkpd/v2s+fwzeooTVzpwTOC7775LgwYNooc5SSlAuGKdhVPtv5+5Nb/44gt6qePjFMa8riKCgDsQyIc54bPP6Dd+cfyaCx1MmPULlWFmlkaVKhLCbKry8zA9c0JqbX2nbx+a36Ub9WPe2R9+/DG1XWWbzhFwvQakM0DgxoLy+X7/Z6l2+XLkx0rhRXYvHzxxknbwts379tGShQsIRM9aXEoou5wLoqZxvnyUkwm4IzlmJpgfArBkonoRyhHivJuZkmjXnj0KkUrFi9PHAwdQJ66dHehGMmZ3u+B1NrzSXCcRAFE4kkaaMmn4Wq7tvoG9AZOYRHzcD4kPgjzsQm9QoTw9zNx9zWvUcPjsuWNiaM7bY2guE06/NXW6qu6FF7x6bCF9oGZNKleunHq5y89uPj16BBwGwkU7vszZwqGM34DH2rrojHIaQcA+ApgTHuEKZ0gyxZywnn/DyeeEhhUr0MMchtGsRnX7J3FibQ5+nkIJ7f2/d+jbb7+lxx9/3ImjZVc9IWB6BVRT2KqVKpmE+qgyK4y2co0VS/Bgbudkjd3shgAJM9z0u5nj7uzFi3SF3RQghEYsVgRXlcjNxMt1S5agF7jWbUN+W0RmsCdELKCeQNn41wBPKMomaqUTTx49Snu2baOd7Eqfx+wHU+YvoA1cjcvZuDAorfiPF7uZfy+hxRs2KALqa5ZsW1/+/RQrWpQqsFeiBu/Xmrktc3F9c5F7CCxdulTFfuKlOZRDYUQEAU8gkHxOOMEVk/Zu367mhN94Tvhq3nzaOGUyFc+bN8PN6dr0IZrzzz/01FNPURWmhSrMllgR4yEgCigHPjsiAUyiXKFIEfXfkf0zax9Noc6s68t1jYlALCuB+A+ewF2cPPfmwIGUkIHM9tLsQcD/V6iL8izs5Upb8DiA2Hzrvv20nh8+Uzls5espU2gFW2G9UVavXq0sNIsXL+IY8l2EErox/OIJWrUWLVuqcIOodIQspNbXq8yv2oMzy8H72btVy9R2tW5zbIaz7i4LgoBDCMQxbyf+Y07Ai+lbHFaTkTkh+UU/H/wiVe39NIfqtObKaisphA07IsZCwPQKqLGGU3ojCLgfAVe/5ID3tjA/yPDf1q3fh2NGV+0/4P4OOXkFVB4azWUDoYBGMQsE4uHaPN5BJQQdP3uOVm7ZSv3791eVzbp166YSKvK6wCqEZvbr148OHDhAKyZ8IqEKTo6b7K4vBOBJ/HHUSKrff4AqajGHOaezuzF8TV/oGKO1ooAaYxylF4KA4RBwtaKbUYBA+D7kpZdoFdOoVS5RnL577VVqVasmgUQ7uRzluHIkcX3CVtwvv/yS+jK9DOI2o9lCml753//+R5MnT6aPBj5HZQoWTO9p5DhBQDcIlGfX+w8jX6NHXh5OLVu0oJmcCIV8CxFjIOAxGiYk+izgWq+gIkpNsB1USMmz0pHcs5wDoP9h1xxI3EUEAUFAEPAEAqBle7BJE6rPpUuv8Dw2883R9M8nH1ObunXsKp9oUy5WNEf26E67vp1Gg9o9Rp8xu0ABDjkYMWKESlJ0pt2I636ViwW8xMrvc5x01LuVd9AuOdMH2VcQSC8CjatUpp9Gv07/8PO/GpcO3rp1a3pPJcd5GQIeUUBRkg+uKHBtDh48mGYyxYs9GcecY++8844q4dejRw+VSY79UE+6K5d4XLx4sYoLwzkk2cYegrJOEBAEXIEA5pzvvvuO6nBmL5Khdm/dQpOHDVF0UrZhAmldK5ytNa+zIrpj2jfUuVFDepu5O/Mwf26fPn0IBSnSknXr1lEDVnxff/11GtK5k8oOTusY2S4IGA2BB6tWpb8+fJ8uM8MMdAkRYyDgERf8+PHjVcwUqFZQG7onVwFq3rw5+fn5WVEErx2yO2fMmKFqoWPynzZtmoqfAnk1yNsHDBig9kdm3KpVq6h69YxTPlgbIAuCgCAgCDAC+zhDvzxzl15kdotKxYvRlJeHUbv69TIUcwlqmQ+e609DOnWkj36eSZOZXmYCW0VzMY1b3Xr1FM9wbo6BRYzbpUuXlKdoEdfFXsPZxfm47vqsN99wCcWNDLAgoFcE4I5vwAl+6w4f0WsXpN3JEMjClsS7yda59OstrhGMeud/MAm7FtPVvn17QhUPuKQ0WbRoES1ZsoRee+01tQrW0lFce/ybb76h4cOHUxN2gdWpU0dt+4yJcUHX0t2mzvAnn3xCG5l3E4KkBuwTw9yDaQlqVJ87d44C/XwpaxaPGITTalKGtl/mUIUszGWa1U5cWoZOLAcLAhYEMGXc4d+Nu38zCQi1YZaKKAfiJjHPgJ937969dscJ3LyYD5yZE3z5N+TPvyV3CDC8yhg6MvkG8Yu6Nnempy0yJ6QHNTnGGQTucjXBO/x79cicwM93R9glMCeEcZLgHgsXtzP9kX09g4B7ZlebtqMSUBBz1dlOoLgp8ECwVUCPMQ0L1muCh8mZM2fU1+PMuYnvmmAZHJy2gpsNyiTEnymTHI0T3cA8hJ06daIfuPpLSa70ondx9n0CD2xwLaKqCqzMZpNTp05RA65TjvCPRsx7aTZBbHXlypWVi/eRRx5xqvu2v2mnDnRiZ2eugRfPlATFIhydExAyhJK58MYUT8YHnNL5vXm9s3PC7t27CfcCkqfAwWg2wTOrIVMLvf/++2puMFv/QfWFZwGYHvBscEac+b06c17bfZ25Rmpzgu05ZTlzEHC7AoqqJiBotxUoi8npFJLvh320Mn2pbdPOC9qT9AhuZlQ4wo1qxgos6LPWfx83WXvSMy6eOsbs/cfvDOOP34HRx9+Zh5H2m5A5we2PCE/91B2+jjYnmOE3YQ8Us/ffHiayzj0IpKMRbKMAAAqKSURBVGwycNH1YCq/wqUtYWnRBNbP5NVN4BrDek2wjDKXEFCXJN+W/HjtOPkUBAQBQUAQEAQEAUFAEPBuBNyugMKqAnM+yJshiPOMiIhQ//EdyUeoIAJXz2aupnDo0CGCVebXX3+lqpz5BqnNmajz5s1T+4GeCVRMqDbiCgkPD6eHHnooiYvfFefVyznAqYb+OxJTo5c+OdNOWOLR/xyc6GFGgVUQ/Y+Pjzdj9+32GfMTMDFr5RX028xzAkK40P84D5VPtnsTZuJKPLPRfyTFiQgC7kTA7UlIaDwqd4A6CaZ9PPDAhVeU6z1DmjZtqhKSkCE/Z84c+vDDDwnxWvny5VMxKPgxQCEdOXKkUlBxfIcOHeixxx5Tx8sfQUAQEAQEAUFAEBAEBAF9IeARBVSD5Pz58wSLY2qCRAG46+1VOwA9CeJCjR6rlho+sk0QEAQEAUFAEBAEBAG9I+BRBdQbwULlJVhoK1asmKEyed7Yt7TadPDgQTp69Kh1N7jhixQpYv1u5IXt27erRLj8+fNbu4nwjrXMu4h1xYoVs6434oK9/iP72zZWG14KeCPMJjInyJyg3fNmnhMuX76svI4aFvgU7m1bNGQ5owhke40loyfR6/Gg3pnFtWVB3/Txxx9TzZo1k1BB6bVfjrb7vffeo2XLlqk4XCgkCHUoW7aso4frdj8QjaOoQUGup60p3FC+sA4WenDIIja0RIkSuu1jag2313+MfZcuXejChQuEewH/QZNmtjg4mRNkTpA5IdEIgdLXKCIDWiptTmjcuHFqU4tsEwScQsB8HBsWeJD8lFLlJacQ1PHOu3btorfffpvy5s2r41441/RffvmFJk+efN+LhiPVupy7knfunVL/8XtAIhLuB7OKzAlEMifcu/vNPifgXmjVqpV6Mb2HiiwJAq5DwO1Z8K5rqmvPBAJ2WPs0bkC44Ldu3erai3jx2UA2DGorELFPnTr1PmJ/L256hpqGGOJJkyYpy6dGaAzrHwobaNZfWP0CAwPpyBHjlXyz138AiocNFND58+cTlFTcH2YTmRNkTpA5IYv1Z485AbkY06dPp9WrV5OzBQ2sJ5IFQSAFBEyrgKZWeSkFrAy1GuXJEO+HiQWFAuB+njt3rqH6aK8zKOmqUU5pE2pq1brsnUPP6+z1H/1B6VvEPmp1yNu1a0eIfzOTyJwgc4LMCfeKw0IBXblypaos+PXXX9OLL75opulA+uoBBEzrggcllG2FJljBtMpLHsA90y+B+MaZM2da+VgLFy6sSu81b94809vm6QYkvxdwfdwPyat1ebpdnrxer169CP9h+YXg5QTWUJSkNIskvw9kTpA5wfbeN9ucgFKsiImHl7Bly5aqLCc8RcIZbHtXyHJGEDCtBTS1yksZAVQvx4IS69y5c9bmIg70xIkTqiyjdaVJFhyt1mVkOBBuYJsBj/sBFkEzicwJMido97vZ5wQk5oIdRgtR8/PzUwmJx48f1yCST0EgwwiYVgFNrfJShlHVwQmQ7Txw4EC6du2aiu1BEYC6detaJxwddMFlTUyrWpfLLuTFJ0KFsgkTJqgWIv5z0aJFVL9+fS9useubJnOCzAnaXWX2OcHX15fGjh2rXPDAZNu2bXTmzBkqX768BpF8CgIZRsC0LvjQ0FDq3bs39ezZ01p5qWPHjhkGVC8nKFSoELVp00ZhANcS8Bg1apRemu/ydvbt21dV60JYAt76Ua3LTILKYsiA79atm0pMA90KEvPMJDInyJxge7+beU5AMhbyAiZOnKj+I1l1yJAhUgTG9gaR5QwjYHoi+tQqL2UYXR2c4M6dOwTCYTx8RYgcqdZlZJxg/UQsJOphm1VkTpA5wfbeN/ucAG8Zng8aQ4AtNrIsCGQEAdMroBkBT44VBAQBQUAQEAQEAUFAEHAeAdPGgDoPlRwhCAgCgoAgIAgIAoKAIOAKBEQBdQWKcg5BQBAQBAQBQUAQEAQEAYcREAXUYahkR0FAEBAEBAFBQBAQBAQBVyAgCqgrUJRzCAKCgCAgCAgCgoAgIAg4jIAooA5DJTsKAoKAHhD46KOP6N9//9VDU6WNgoAgIAiYFgFRQE079NJxQcCYCHz44YdWAm1j9lB6JQgIAoKA/hEQBVT/Yyg9EAQEAUFAEBAEBAFBQFcIiAKqq+GSxgoC5kNgw4YN1KtXL2rSpImqznL06NEkICxcuJB69OhBqOa0YMGCJNuWLl2qKlzZrpw1a5aq+qStO336NI0ZM4Zatmypqr2sXbtW2ySfgoAgIAgIAm5CQBRQNwErpxUEBIGMI4Ca9DVq1FDVuqBgrlq1isqWLUuaEjp//nxq1aoV3b17lypXrkzdu3enAwcOWC+8fft2mj59uvU7FtasWUOzZ89W665cuUJNmzalb775hh566CG6fv061axZk/bt25fkGPkiCAgCgoAg4FoETFsL3rUwytkEAUHAHQi88MILSkH89ttv1elhCUWN+jfffJOQbIR61cOGDaMRI0ao7e3ataOiRYs63JRJkybRiRMnaPfu3eTn56eOu3HjBv3xxx/K6urwiWRHQUAQEAQEAacQEAXUKbhkZ0FAEPAUArBG/vfff5QzZ07lGteui1r1sGLCerlr1y5q0KCBtokKFCjglAK6fv16ql27tlX5xIk+/vhj6/lkQRAQBAQBQcA9CIgC6h5c5ayCgCCQQQQuXrxId+7coeDgYMqa9V60UOPGjSkiIoIuXbqktt+6dSvJlXx9fZN8h3veVm7evGn9CutnfHy89bssCAKCgCAgCHgGAVFAPYOzXEUQEAScRCAmJoZCQ0MpV65cyuWuHY6kIyiZOXLkUP/xvV69emrzqVOnaOPGjdqu5O/vr+JHrSt4wTa+s2DBgrRp0ybbzTR27Fi6evUqDR8+PMl6+SIICAKCgCDgOgTumRVcd045kyAgCAgCLkGgT58+KkEISUO3b9+mJUuWUOvWrQmZ65CuXbuqJKO//vqLzp07R6+++qpKSNIujnhQWFKnTJlCsHziPHPmzNE2q+z5ZcuW0bhx45RLHxn3o0aNUglN1p1kQRAQBAQBQcDlCGRh91RS/5TLLyEnFAQEAUEgfQhcu3aNXnzxRZo4cSL5+PhQXFwcPfHEE/T666+rE8JF/9RTTyklFAoqqJqOHDlCXbp0of79+6t98PnJJ5+o5XLlyikFFnRNy5cvV+umTZum9kVMaWxsLHXr1o1GjhyptskfQUAQEAQEAfcgIAqoe3CVswoCgoALEYD1MrV4zYSEBOVqj46OtntVuNQRMwoF1p7gPRyKa+7cuSlLliz2dpF1goAgIAgIAi5EQBRQF4IppxIEBAFBQBAQBAQBQUAQSBsBiQFNGyPZQxAQBAQBQUAQEAQEAUHAhQiIAupCMOVUgoAgIAgIAoKAICAICAJpIyAKaNoYyR6CgCAgCAgCgoAgIAgIAi5EQBRQF4IppxIEBAFBQBAQBAQBQUAQSBsBUUDTxkj2EAQEAUFAEBAEBAFBQBBwIQKigLoQTDmVICAICAKCgCAgCAgCgkDaCIgCmjZGsocgIAgIAoKAICAICAKCgAsREAXUhWDKqQQBQUAQEAQEAUFAEBAE0kbg/2s3WDOc2dZ9AAAAAElFTkSuQmCC" alt="Density plot for educ before and after matching." /></p>
-<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Bar graph for categorical variables</span></span>
-<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a><span class="fu">bal.plot</span>(m.out, <span class="at">var.name =</span> <span class="st">"race"</span>, <span class="at">which =</span> <span class="st">"both"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" tabindex="-1"></a><span class="co">#Density plot for continuous variables</span></span>
+<span id="cb24-2"><a href="#cb24-2" tabindex="-1"></a><span class="fu">bal.plot</span>(m.out, <span class="at">var.name =</span> <span class="st">"educ"</span>, <span class="at">which =</span> <span class="st">"both"</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,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" alt="Density plot for educ before and after matching." /></p>
+<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" tabindex="-1"></a><span class="co">#Bar graph for categorical variables</span></span>
+<span id="cb25-2"><a href="#cb25-2" tabindex="-1"></a><span class="fu">bal.plot</span>(m.out, <span class="at">var.name =</span> <span class="st">"race"</span>, <span class="at">which =</span> <span class="st">"both"</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" alt="Bar graph for race before and after matching." /></p>
-<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Mirrored histogram</span></span>
-<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a><span class="fu">bal.plot</span>(m.out, <span class="at">var.name =</span> <span class="st">"distance"</span>, <span class="at">which =</span> <span class="st">"both"</span>,</span>
-<span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a> <span class="at">type =</span> <span class="st">"histogram"</span>, <span class="at">mirror =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a><span class="co">#Mirrored histogram</span></span>
+<span id="cb26-2"><a href="#cb26-2" tabindex="-1"></a><span class="fu">bal.plot</span>(m.out, <span class="at">var.name =</span> <span class="st">"distance"</span>, <span class="at">which =</span> <span class="st">"both"</span>,</span>
+<span id="cb26-3"><a href="#cb26-3" tabindex="-1"></a> <span class="at">type =</span> <span class="st">"histogram"</span>, <span class="at">mirror =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
<p><img 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" alt="Mirrored histograms of propensity scores before and after matching." /></p>
<p>These plots help illuminate the specific ways in which the covariate
distributions differ between treatment groups, which can aid in
diff --git a/inst/doc/estimating-effects.R b/inst/doc/estimating-effects.R
index bca1a36..23de241 100644
--- a/inst/doc/estimating-effects.R
+++ b/inst/doc/estimating-effects.R
@@ -7,6 +7,7 @@ me_ok <- requireNamespace("marginaleffects", quietly = TRUE) &&
su_ok <- requireNamespace("survival", quietly = TRUE)
boot_ok <- requireNamespace("boot", quietly = TRUE)
+## ---- include = FALSE---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#Generating data similar to Austin (2009) for demonstrating treatment effect estimation
gen_X <- function(n) {
X <- matrix(rnorm(9 * n), nrow = n, ncol = 9)
@@ -103,12 +104,11 @@ avg_comparisons(fit1, variables = "A",
newdata = subset(md, A == 1),
wts = "weights")
-## ---- eval=me_ok && packageVersion("marginaleffects") > "0.10.0"--------------------------------------------------------------------------------------------------------------------------------------
-# avg_predictions(fit1, variables = "A",
-# vcov = ~subclass,
-# newdata = subset(md, A == 1),
-# wts = "weights",
-# by = "A")
+## ---- eval=me_ok && packageVersion("marginaleffects") >= "0.11.0"-------------------------------------------------------------------------------------------------------------------------------------
+avg_predictions(fit1, variables = "A",
+ vcov = ~subclass,
+ newdata = subset(md, A == 1),
+ wts = "weights")
## ---- eval=me_ok--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#Subclassification on the PS for the ATT
@@ -141,8 +141,8 @@ avg_comparisons(fit2,
vcov = ~subclass,
newdata = subset(md, A == 1),
wts = "weights",
- transform_pre = "lnratioavg",
- transform_post = "exp")
+ comparison = "lnratioavg",
+ transform = "exp")
## ---- eval=su_ok--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
library("survival")
diff --git a/inst/doc/estimating-effects.Rmd b/inst/doc/estimating-effects.Rmd
index 22e71ac..ed0fded 100644
--- a/inst/doc/estimating-effects.Rmd
+++ b/inst/doc/estimating-effects.Rmd
@@ -32,7 +32,8 @@ me_ok <- requireNamespace("marginaleffects", quietly = TRUE) &&
requireNamespace("sandwich", quietly = TRUE)
su_ok <- requireNamespace("survival", quietly = TRUE)
boot_ok <- requireNamespace("boot", quietly = TRUE)
-
+```
+```{r, include = FALSE}
#Generating data similar to Austin (2009) for demonstrating treatment effect estimation
gen_X <- function(n) {
X <- matrix(rnorm(9 * n), nrow = n, ncol = 9)
@@ -107,7 +108,7 @@ This guide is structured as follows: first, information on the concepts related
Before an effect is estimated, the estimand must be specified and clarified. Although some aspects of the estimand depend not only on how the effect is estimated after matching but also on the matching method itself, other aspects must be considered at the time of effect estimation and interpretation. Here, we consider three aspects of the estimand: the population the effect is meant to generalize to (the target population), the effect measure, and whether the effect is marginal or conditional.
-**The target population.** Different matching methods allow you to estimate effects that can generalize to different target populations. The most common estimand in matching the average treatment effect in the treated (ATT), which is the average effect of treatment for those who receive treatment. This estimand is estimable for matching methods that do not change the treated units (i.e., by weighting or discarding units) and is requested in `matchit()` by setting `estimand = "ATT"` (which is the default). The average treatment effect in the population (ATE) is the average effect of treatment for the population from which the sample is a random sample. This estimand is estimable only for methods that allow the ATE and either do not discard units from the sample or explicit target full sample balance, which in `MatchIt` is limited to full matching, subclassification, and template matching when setting `estimand = "ATE"`. When treated units are discarded (e.g., through the use of common support restrictions, calipers, cardinality matching, or [coarsened] exact matching), the estimand corresponds to neither the population ATT nor the population ATE, but rather to an average treatment effect in the remaining matched sample (ATM), which may not correspond to any specific target population. See @greiferChoosingEstimandWhen2021 for a discussion on the substantive considerations involved when choosing the target population of the estimand.
+**The target population.** Different matching methods allow you to estimate effects that can generalize to different target populations. The most common estimand in matching is the average treatment effect in the treated (ATT), which is the average effect of treatment for those who receive treatment. This estimand is estimable for matching methods that do not change the treated units (i.e., by weighting or discarding units) and is requested in `matchit()` by setting `estimand = "ATT"` (which is the default). The average treatment effect in the population (ATE) is the average effect of treatment for the population from which the sample is a random sample. This estimand is estimable only for methods that allow the ATE and either do not discard units from the sample or explicit target full sample balance, which in `MatchIt` is limited to full matching, subclassification, and template matching when setting `estimand = "ATE"`. When treated units are discarded (e.g., through the use of common support restrictions, calipers, cardinality matching, or [coarsened] exact matching), the estimand corresponds to neither the population ATT nor the population ATE, but rather to an average treatment effect in the remaining matched sample (ATM), which may not correspond to any specific target population. See @greiferChoosingEstimandWhen2021 for a discussion on the substantive considerations involved when choosing the target population of the estimand.
**Marginal and conditional effects.** A marginal effect is a comparison between the expected potential outcome under treatment and the expected potential outcome under control. This is the same quantity estimated in randomized trials without blocking or covariate adjustment and is particularly useful for quantifying the overall effect of a policy or population-wide intervention. A conditional effect is the comparison between the expected potential outcomes in the treatment groups within strata. This is useful for identifying the effect of a treatment for an individual patient or a subset of the population.
@@ -145,7 +146,7 @@ To compute SEs after g-computation, a method known as the delta method is used;
**Robust standard errors.** Also known as sandwich SEs (due to the form of the formula for computing them), heteroscedasticity-consistent SEs, or Huber-White SEs, robust SEs are an adjustment to the usual maximum likelihood or ordinary least squares SEs that are robust to violations of some of the assumptions required for usual SEs to be valid [@mackinnon1985]. Although there has been some debate about their utility [@king2015], robust SEs rarely degrade inferences and often improve them. Generally, robust SEs **must** be used when any non-uniform weights are included in the estimation (e.g., with matching with replacement or inverse probability weighting).
-**Cluster-robust standard errors.** A version of robust SEs known as cluster-robust SEs [@liang1986] can be used to account for dependence between observations within clusters (e.g., matched pairs). @abadie2019 demonstrate analytically that cluster-robust SEs are generally valid after matching, whereas regular robust SEs can over- or under-estimate the true sampling variability of the effect estimator depending on the specification of the outcome model (if any) and degree of effect modification. A plethora of simulation studies have further confirmed the validity of cluster-robust SEs after matching [e.g., @austin2009a; @austin2014; @gayat2012; @wan2019; @austin2013]. Given this evidence favoring the use of cluster-robust SEs, we recommend them in most cases and use them judiciously in the this guide[^1].
+**Cluster-robust standard errors.** A version of robust SEs known as cluster-robust SEs [@liang1986] can be used to account for dependence between observations within clusters (e.g., matched pairs). @abadie2019 demonstrate analytically that cluster-robust SEs are generally valid after matching, whereas regular robust SEs can over- or under-estimate the true sampling variability of the effect estimator depending on the specification of the outcome model (if any) and degree of effect modification. A plethora of simulation studies have further confirmed the validity of cluster-robust SEs after matching [e.g., @austin2009a; @austin2014; @gayat2012; @wan2019; @austin2013]. Given this evidence favoring the use of cluster-robust SEs, we recommend them in most cases and use them judiciously in this guide[^1].
[^1]: Because they are only appropriate with a large number of clusters, cluster-robust SEs are generally not used with subclassification methods. Regular robust SEs are valid with these methods when using the subclassification weights to estimate marginal effects.
@@ -173,7 +174,7 @@ head(d)
We will need to the following packages to perform the desired analyses:
-- `marginaleffects` provides the `comparisons()` function for performing g-computation and estimating the SEs and confidence intervals of the average estimate potential outcomes and treatment effects
+- `marginaleffects` provides the `avg_comparisons()` function for performing g-computation and estimating the SEs and confidence intervals of the average estimate potential outcomes and treatment effects
- `sandwich` is used internally by `marginaleffects` to compute robust and cluster-robust SEs
- `survival` provides `coxph()` to estimate the coefficients in a Cox-proportional hazards model for the marginal hazard ratio, which we will use for survival outcomes.
@@ -187,7 +188,7 @@ library("MatchIt")
library("marginaleffects")
```
-All effect estimates will be computed using `marginaleffects::comparions()`, even when its use may be superfluous (e.g., for performing a t-test in the matched set). As previously mentioned, this is because it is useful to have a single workflow that works no matter the situation, perhaps with very slight modifications to accommodate different contexts. Using `comparisons()` has several advantages, even when the alternatives are simple: it only provides the effect estimate, and not other coefficients; it automatically incorporates robust and cluster-robust SEs if requested; and it always produces average marginal effects for the correct population if requested.
+All effect estimates will be computed using `marginaleffects::avg_comparions()`, even when its use may be superfluous (e.g., for performing a t-test in the matched set). As previously mentioned, this is because it is useful to have a single workflow that works no matter the situation, perhaps with very slight modifications to accommodate different contexts. Using `avg_comparions()` has several advantages, even when the alternatives are simple: it only provides the effect estimate, and not other coefficients; it automatically incorporates robust and cluster-robust SEs if requested; and it always produces average marginal effects for the correct population if requested.
Other packages may be of use but are not used here. There are alternatives to the `marginaleffects` package for computing average marginal effects, including `margins` and `stdReg`. The `survey` package can be used to estimate robust SEs incorporating weights and provides functions for survey-weighted generalized linear models and Cox-proportional hazards models. It is often used with propensity score weighting.
@@ -229,7 +230,7 @@ fit1 <- lm(Y_C ~ A * (X1 + X2 + X3 + X4 + X5 +
data = md, weights = weights)
```
-Next, we use `marginaleffects::comparisons()` to estimate the ATT.
+Next, we use `marginaleffects::avg_comparisons()` to estimate the ATT.
```{r, eval=me_ok}
avg_comparisons(fit1, variables = "A",
@@ -238,21 +239,20 @@ avg_comparisons(fit1, variables = "A",
wts = "weights")
```
-Let's break down the call to `avg_comparisons()`: to the first argument, we supply the model fit, `fit`; to the `variables` argument, the name of the treatment (`"A"`); to the `vcov` argument, a formula with subclass membership (`~subclass`) to request cluster-robust SEs; to the `newdata` argument, a version of the matched dataset containing only the treated units (`subset(md, A == 1)`) to request the ATT; and to the `wts` argument, the names of the variable in `md` containing the matching weights (`"weights"`) to ensure they are included in the analysis. Some of these arguments differ depending on the specifics of the matching method and outcome type; see the sections below for information.
+Let's break down the call to `avg_comparisons()`: to the first argument, we supply the model fit, `fit1`; to the `variables` argument, the name of the treatment (`"A"`); to the `vcov` argument, a formula with subclass membership (`~subclass`) to request cluster-robust SEs; to the `newdata` argument, a version of the matched dataset containing only the treated units (`subset(md, A == 1)`) to request the ATT; and to the `wts` argument, the names of the variable in `md` containing the matching weights (`"weights"`) to ensure they are included in the analysis. Some of these arguments differ depending on the specifics of the matching method and outcome type; see the sections below for information.
If, in addition to the effect estimate, we want the average estimated potential outcomes, we can use `marginaleffects::avg_predictions()`, which we demonstrate below. Note the interpretation of the resulting estimates as the expected potential outcomes is only valid if all covariates present in the outcome model (if any) are interacted with the treatment.
-```{r, eval=me_ok && packageVersion("marginaleffects") > "0.10.0"}
+```{r, eval=me_ok && packageVersion("marginaleffects") >= "0.11.0"}
avg_predictions(fit1, variables = "A",
vcov = ~subclass,
newdata = subset(md, A == 1),
- wts = "weights",
- by = "A")
+ wts = "weights")
```
-We can see that the difference in potential outcome means is equal to the average treatment effect computed previously[^4]. Almost all of the arguments to `avg_predictions()` are the same as those to `avg_comparisons()`; the only difference is that the `by` argument needs to be specified naming the treatment. (Note: due to a bug, the above code will not evaluate unless `marginaleffects` is a version greater than 0.10.0.)
+We can see that the difference in potential outcome means is equal to the average treatment effect computed previously[^4]. All of the arguments to `avg_predictions()` are the same as those to `avg_comparisons()`.
-[^4]: To verify that they are equal, add the argument `hypothesis = "revpairwise"` to the call to `predictions()`; this explicitly compares the average potential outcomes and should yield identical estimates to the `comparisons()` call.
+[^4]: To verify that they are equal, supply the output of `avg_predictions()` to `hypotheses(), e.g., `avg_predictions(...) |> hypotheses("revpairwise")`; this explicitly compares the average potential outcomes and should yield identical estimates to the `avg_comparisons()` call.
### Adjustments to the Standard Case
@@ -266,7 +266,7 @@ When matching for the ATE (including [coarsened] exact matching, full matching,
When matching with replacement (i.e., nearest neighbor or genetic matching with `replace = TRUE`), effect and SE estimation need to account for control unit multiplicity (i.e., repeated use) and within-pair correlations [@hill2006; @austin2020a]. Although @abadie2008 demonstrated analytically that bootstrap SEs may be invalid for matching with replacement, simulation work by @hill2006 and @bodory2020 has found that bootstrap SEs are adequate and generally slightly conservative. See the section "Using Bootstrapping to Estimate Confidence Intervals" for instructions on using the bootstrap and an example that use matching with replacement.
-Because control units do not belong to unique pairs, there is no pair membership in the `match.data()` output. One can simply change `vcov = ~subclass` to `vcov = "HC3"` in the calls to `comparisons()` and `predictions()` to use robust SEs instead of cluster-robust SEs, as recommended by @hill2006. There is some evidence for an alternative approach that incorporates pair membership and adjusts for reuse of control units, though this has only been studied for survival outcomes [@austin2020a]. This adjustment involves using two-way cluster-robust SEs with pair membership and unit ID as the clustering variables. For continuous and binary outcomes, this involves the following two changes: 1) replace `match.data()` with `get_matches()`, which produces a dataset with a row per unit per pair, meaning control units matched to multiple treated units will appear multiple times in the dataset; 2) set `vcov = ~subclass + id` in the calls to `avg_comparisons()` and `avg_predictions()`. For survival outcomes, a special procedure must be used; see the section on survival outcomes below.
+Because control units do not belong to unique pairs, there is no pair membership in the `match.data()` output. One can simply change `vcov = ~subclass` to `vcov = "HC3"` in the calls to `comparisons()` and `predictions()` to use robust SEs instead of cluster-robust SEs, as recommended by @hill2006. There is some evidence for an alternative approach that incorporates pair membership and adjusts for reuse of control units, though this has only been studied for survival outcomes [@austin2020a]. This adjustment involves using two-way cluster-robust SEs with pair membership and unit ID as the clustering variables. For continuous and binary outcomes, this involves the following two changes: 1) replace `match.data()` with `get_matches()`, which produces a dataset with one row per unit per pair, meaning control units matched to multiple treated units will appear multiple times in the dataset; 2) set `vcov = ~subclass + id` in the calls to `avg_comparisons()` and `avg_predictions()`. For survival outcomes, a special procedure must be used; see the section on survival outcomes below.
#### Matching without pairing
@@ -313,7 +313,7 @@ To fit a logistic regression model, change `lm()` to `glm()` and set `family = q
[^7]: Note that for low or high average expected risks computed with `predictions()`, the confidence intervals may go below 0 or above 1; this is because an approximation is used. To avoid this problem, bootstrapping or simulation-based inference can be used instead.
-To compute the marginal RR, we need to add `transform_pre = "lnratioavg"` to `avg_comparisons()`; this computes the marginal log RR. To get the marginal RR, we need to add `transform_post = "exp"` to `avg_comparisons()`, which exponentiates the marginal log RR and its confidence interval. The code below computes the effects and displays the statistics of interest:
+To compute the marginal RR, we need to add `comparison = "lnratioavg"` to `avg_comparisons()`; this computes the marginal log RR. To get the marginal RR, we need to add `transform = "exp"` to `avg_comparisons()`, which exponentiates the marginal log RR and its confidence interval. The code below computes the effects and displays the statistics of interest:
```{r, eval=me_ok}
#Logistic regression model with covariates
@@ -328,13 +328,13 @@ avg_comparisons(fit2,
vcov = ~subclass,
newdata = subset(md, A == 1),
wts = "weights",
- transform_pre = "lnratioavg",
- transform_post = "exp")
+ comparison = "lnratioavg",
+ transform = "exp")
```
-The output displays the marginal RR, its Z-value, the p-value for the Z-test against 0, and its confidence interval. (Note that even though the `Contrast` label still suggests the log risk ratio, the risk ratio is actually displayed. You can confirm this by manually exponentiating the log risk ratio computed just before.) To view the log RR and its standard error, omit the `transform_post` argument.
+The output displays the marginal RR, its Z-value, the p-value for the Z-test of the log RR against 0, and its confidence interval. (Note that even though the `Contrast` label still suggests the log RR, the RR is actually displayed.) To view the log RR and its standard error, omit the `transform` argument.
-For the marginal OR, the only thing that needs to change is that `transform_pre` should be set to `"lnoravg"`.
+For the marginal OR, the only thing that needs to change is that `comparison` should be set to `"lnoravg"`.
#### Survival outcomes
@@ -396,7 +396,7 @@ The bootstrap is an alternative to the delta method for estimating confidence in
For the standard bootstrap, we need a function that takes in the original dataset and a vector of sampled unit indices and returns the estimated quantity of interest. This function should perform the matching on the bootstrap sample, fit the outcome model, and estimate the treatment effect using g-computation. In this example, we'll use matching with replacement, since the standard bootstrap has been found to work well with it [@bodory2020; @hill2006], despite some analytic results recommending otherwise [@abadie2008]. We'll implement g-computation manually rather than using `avg_comparisons()`, as this dramatically improves the speed of the estimation since we don't require standard errors to be estimated in each sample (or other processing `avg_comparisons()` does). We'll consider the marginal RR ATT of `A` on the binary outcome `Y_B`.
-The first step is to write the estimation function, we we call `boot_fun`. This function returns the marginal RR. In it, we perform the matching, estimate the effect, and return the estimate of interest.
+The first step is to write the estimation function, we call `boot_fun`. This function returns the marginal RR. In it, we perform the matching, estimate the effect, and return the estimate of interest.
```{r}
boot_fun <- function(data, i) {
diff --git a/inst/doc/estimating-effects.html b/inst/doc/estimating-effects.html
index 464b350..7d4c802 100644
--- a/inst/doc/estimating-effects.html
+++ b/inst/doc/estimating-effects.html
@@ -12,7 +12,7 @@
<meta name="author" content="Noah Greifer" />
-<meta name="date" content="2023-02-22" />
+<meta name="date" content="2023-06-13" />
<title>Estimating Effects After Matching</title>
@@ -362,7 +362,7 @@ code > span.er { color: #a61717; background-color: #e3d2d2; }
<h1 class="title toc-ignore">Estimating Effects After Matching</h1>
<h4 class="author">Noah Greifer</h4>
-<h4 class="date">2023-02-22</h4>
+<h4 class="date">2023-06-13</h4>
<div id="TOC">
@@ -446,26 +446,27 @@ target population), the effect measure, and whether the effect is
marginal or conditional.</p>
<p><strong>The target population.</strong> Different matching methods
allow you to estimate effects that can generalize to different target
-populations. The most common estimand in matching the average treatment
-effect in the treated (ATT), which is the average effect of treatment
-for those who receive treatment. This estimand is estimable for matching
-methods that do not change the treated units (i.e., by weighting or
-discarding units) and is requested in <code>matchit()</code> by setting
-<code>estimand = "ATT"</code> (which is the default). The average
-treatment effect in the population (ATE) is the average effect of
-treatment for the population from which the sample is a random sample.
-This estimand is estimable only for methods that allow the ATE and
-either do not discard units from the sample or explicit target full
-sample balance, which in <code>MatchIt</code> is limited to full
-matching, subclassification, and template matching when setting
-<code>estimand = "ATE"</code>. When treated units are discarded (e.g.,
-through the use of common support restrictions, calipers, cardinality
-matching, or [coarsened] exact matching), the estimand corresponds to
-neither the population ATT nor the population ATE, but rather to an
-average treatment effect in the remaining matched sample (ATM), which
-may not correspond to any specific target population. See <span class="citation">Greifer and Stuart (<a href="#ref-greiferChoosingEstimandWhen2021" role="doc-biblioref">2021</a>)</span> for a discussion on the
-substantive considerations involved when choosing the target population
-of the estimand.</p>
+populations. The most common estimand in matching is the average
+treatment effect in the treated (ATT), which is the average effect of
+treatment for those who receive treatment. This estimand is estimable
+for matching methods that do not change the treated units (i.e., by
+weighting or discarding units) and is requested in
+<code>matchit()</code> by setting <code>estimand = "ATT"</code> (which
+is the default). The average treatment effect in the population (ATE) is
+the average effect of treatment for the population from which the sample
+is a random sample. This estimand is estimable only for methods that
+allow the ATE and either do not discard units from the sample or
+explicit target full sample balance, which in <code>MatchIt</code> is
+limited to full matching, subclassification, and template matching when
+setting <code>estimand = "ATE"</code>. When treated units are discarded
+(e.g., through the use of common support restrictions, calipers,
+cardinality matching, or [coarsened] exact matching), the estimand
+corresponds to neither the population ATT nor the population ATE, but
+rather to an average treatment effect in the remaining matched sample
+(ATM), which may not correspond to any specific target population. See
+<span class="citation">Greifer and Stuart (<a href="#ref-greiferChoosingEstimandWhen2021">2021</a>)</span> for a
+discussion on the substantive considerations involved when choosing the
+target population of the estimand.</p>
<p><strong>Marginal and conditional effects.</strong> A marginal effect
is a comparison between the expected potential outcome under treatment
and the expected potential outcome under control. This is the same
@@ -497,8 +498,8 @@ should not be used when marginal effects are desired.</p>
<div id="g-computation" class="section level3">
<h3>G-computation</h3>
<p>To estimate marginal effects, we use a method known as g-computation
-<span class="citation">(<a href="#ref-snowdenImplementationGComputationSimulated2011" role="doc-biblioref">Snowden, Rose, and Mortimer 2011</a>)</span> or
-regression estimation <span class="citation">(<a href="#ref-schaferAverageCausalEffects2008" role="doc-biblioref">Schafer
+<span class="citation">(<a href="#ref-snowdenImplementationGComputationSimulated2011">Snowden,
+Rose, and Mortimer 2011</a>)</span> or regression estimation <span class="citation">(<a href="#ref-schaferAverageCausalEffects2008">Schafer
and Kang 2008</a>)</span>. This involves first specifying a model for
the outcome as a function of the treatment and covariates. Then, for
each unit, we compute their predicted values of the outcome setting
@@ -549,15 +550,16 @@ Cox proportional hazards model.</p>
covariates in the outcome model. One may ask, why use matching at all if
you are going to model the outcome with covariates anyway? Matching
reduces the dependence of the effect estimate on correct specification
-of the outcome model; this is the central thesis of <span class="citation">Ho et al. (<a href="#ref-ho2007" role="doc-biblioref">2007</a>)</span>. Including covariates in the
-outcome model after matching has several functions: it can increase
-precision in the effect estimate, reduce the bias due to residual
-imbalance, and make the effect estimate “doubly robust”, which means it
-is consistent if either the matching reduces sufficient imbalance in the
-covariates or if the outcome model is correct. For these reasons, we
-recommend covariate adjustment after matching when possible. There is
-some evidence that covariate adjustment is most helpful for covariates
-with standardized mean differences greater than .1 <span class="citation">(<a href="#ref-nguyen2017" role="doc-biblioref">Nguyen
+of the outcome model; this is the central thesis of <span class="citation">Ho et al. (<a href="#ref-ho2007">2007</a>)</span>.
+Including covariates in the outcome model after matching has several
+functions: it can increase precision in the effect estimate, reduce the
+bias due to residual imbalance, and make the effect estimate “doubly
+robust”, which means it is consistent if either the matching reduces
+sufficient imbalance in the covariates or if the outcome model is
+correct. For these reasons, we recommend covariate adjustment after
+matching when possible. There is some evidence that covariate adjustment
+is most helpful for covariates with standardized mean differences
+greater than .1 <span class="citation">(<a href="#ref-nguyen2017">Nguyen
et al. 2017</a>)</span>, so these covariates and covariates thought to
be highly predictive of the outcome should be prioritized in treatment
effect models if not all can be included due to sample size
@@ -576,9 +578,10 @@ covariates in the outcome model. These are not causal effects and their
estimates may be severely confounded. Only the treatment effect estimate
can be interpreted as causal assuming the relevant assumptions about
unconfoundedness are met. Inappropriately interpreting the coefficients
-of covariates in the outcome model is known as the Table 2 fallacy <span class="citation">(<a href="#ref-westreich2013" role="doc-biblioref">Westreich and Greenland 2013</a>)</span>. To avoid
-this, we only display the results of the g-computation procedure and do
-not examine or interpret the outcome models themselves.</p>
+of covariates in the outcome model is known as the Table 2 fallacy <span class="citation">(<a href="#ref-westreich2013">Westreich and Greenland
+2013</a>)</span>. To avoid this, we only display the results of the
+g-computation procedure and do not examine or interpret the outcome
+models themselves.</p>
</div>
<div id="estimating-standard-errors-and-confidence-intervals" class="section level3">
<h3>Estimating Standard Errors and Confidence Intervals</h3>
@@ -610,25 +613,26 @@ as described below.</p>
heteroscedasticity-consistent SEs, or Huber-White SEs, robust SEs are an
adjustment to the usual maximum likelihood or ordinary least squares SEs
that are robust to violations of some of the assumptions required for
-usual SEs to be valid <span class="citation">(<a href="#ref-mackinnon1985" role="doc-biblioref">MacKinnon and White
-1985</a>)</span>. Although there has been some debate about their
-utility <span class="citation">(<a href="#ref-king2015" role="doc-biblioref">King and Roberts 2015</a>)</span>, robust SEs
-rarely degrade inferences and often improve them. Generally, robust SEs
-<strong>must</strong> be used when any non-uniform weights are included
-in the estimation (e.g., with matching with replacement or inverse
-probability weighting).</p>
+usual SEs to be valid <span class="citation">(<a href="#ref-mackinnon1985">MacKinnon and White 1985</a>)</span>. Although
+there has been some debate about their utility <span class="citation">(<a href="#ref-king2015">King and Roberts
+2015</a>)</span>, robust SEs rarely degrade inferences and often improve
+them. Generally, robust SEs <strong>must</strong> be used when any
+non-uniform weights are included in the estimation (e.g., with matching
+with replacement or inverse probability weighting).</p>
<p><strong>Cluster-robust standard errors.</strong> A version of robust
-SEs known as cluster-robust SEs <span class="citation">(<a href="#ref-liang1986" role="doc-biblioref">Liang and Zeger
-1986</a>)</span> can be used to account for dependence between
-observations within clusters (e.g., matched pairs). <span class="citation">Abadie and Spiess (<a href="#ref-abadie2019" role="doc-biblioref">2019</a>)</span> demonstrate analytically that
+SEs known as cluster-robust SEs <span class="citation">(<a href="#ref-liang1986">Liang and Zeger 1986</a>)</span> can be used to
+account for dependence between observations within clusters (e.g.,
+matched pairs). <span class="citation">Abadie and Spiess (<a href="#ref-abadie2019">2019</a>)</span> demonstrate analytically that
cluster-robust SEs are generally valid after matching, whereas regular
robust SEs can over- or under-estimate the true sampling variability of
the effect estimator depending on the specification of the outcome model
(if any) and degree of effect modification. A plethora of simulation
studies have further confirmed the validity of cluster-robust SEs after
-matching <span class="citation">(e.g., <a href="#ref-austin2009a" role="doc-biblioref">Austin 2009</a>, <a href="#ref-austin2013" role="doc-biblioref">2013a</a>; <a href="#ref-austin2014" role="doc-biblioref">Austin and Small 2014</a>; <a href="#ref-gayat2012" role="doc-biblioref">Gayat et al. 2012</a>; <a href="#ref-wan2019" role="doc-biblioref">Wan 2019</a>)</span>. Given this evidence favoring
-the use of cluster-robust SEs, we recommend them in most cases and use
-them judiciously in the this guide<a href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a>.</p>
+matching <span class="citation">(e.g., <a href="#ref-austin2009a">Austin
+2009</a>, <a href="#ref-austin2013">2013a</a>; <a href="#ref-austin2014">Austin and Small 2014</a>; <a href="#ref-gayat2012">Gayat et al. 2012</a>; <a href="#ref-wan2019">Wan
+2019</a>)</span>. Given this evidence favoring the use of cluster-robust
+SEs, we recommend them in most cases and use them judiciously in this
+guide<a href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a>.</p>
</div>
<div id="bootstrapping" class="section level4">
<h4>Bootstrapping</h4>
@@ -637,47 +641,43 @@ delta method is that the delta method is an approximation, as previously
mentioned. One solution to this problem is bootstrapping, which is a
technique used to simulate the sampling distribution of an estimator by
repeatedly drawing samples with replacement and estimating the effect in
-each bootstrap sample <span class="citation">(<a href="#ref-efron1993" role="doc-biblioref">Efron and Tibshirani 1993</a>)</span>. From the
+each bootstrap sample <span class="citation">(<a href="#ref-efron1993">Efron and Tibshirani 1993</a>)</span>. From the
bootstrap distribution, SEs and confidence intervals can be computed in
several ways, including using the standard deviation of the bootstrap
estimates as the SE estimate or using the 2.5 and 97.5 percentiles as
95% confidence interval bounds. Bootstrapping tends to be most useful
when no analytic estimator of a SE is possible or has been derived yet.
-Although <span class="citation">Abadie and Imbens (<a href="#ref-abadie2008" role="doc-biblioref">2008</a>)</span> found
-analytically that the bootstrap is inappropriate for matched samples,
-simulation evidence has found it to be adequate in many cases <span class="citation">(<a href="#ref-hill2006" role="doc-biblioref">Hill and
-Reiter 2006</a>; <a href="#ref-austin2014" role="doc-biblioref">Austin
-and Small 2014</a>; <a href="#ref-austin2017" role="doc-biblioref">Austin and Stuart 2017</a>)</span>.</p>
+Although <span class="citation">Abadie and Imbens (<a href="#ref-abadie2008">2008</a>)</span> found analytically that the
+bootstrap is inappropriate for matched samples, simulation evidence has
+found it to be adequate in many cases <span class="citation">(<a href="#ref-hill2006">Hill and Reiter 2006</a>; <a href="#ref-austin2014">Austin and Small 2014</a>; <a href="#ref-austin2017">Austin and Stuart 2017</a>)</span>.</p>
<p>Typically, bootstrapping involves performing the entire estimation
process in each bootstrap sample, including propensity score estimation,
matching, and effect estimation. This tends to be the most
straightforward route, though intervals from this method may be
conservative in some cases (i.e., they are wider than necessary to
-achieve nominal coverage) <span class="citation">(<a href="#ref-austin2014" role="doc-biblioref">Austin and Small
-2014</a>)</span>. Less conservative and more accurate intervals have
-been found when using different forms of the bootstrap, including the
-wild bootstrap develop by <span class="citation">Bodory et al. (<a href="#ref-bodory2020" role="doc-biblioref">2020</a>)</span> and the
-matched/cluster bootstrap described by <span class="citation">Austin and
-Small (<a href="#ref-austin2014" role="doc-biblioref">2014</a>)</span>
-and <span class="citation">Abadie and Spiess (<a href="#ref-abadie2019" role="doc-biblioref">2019</a>)</span>. The cluster bootstrap involves
+achieve nominal coverage) <span class="citation">(<a href="#ref-austin2014">Austin and Small 2014</a>)</span>. Less
+conservative and more accurate intervals have been found when using
+different forms of the bootstrap, including the wild bootstrap develop
+by <span class="citation">Bodory et al. (<a href="#ref-bodory2020">2020</a>)</span> and the matched/cluster
+bootstrap described by <span class="citation">Austin and Small (<a href="#ref-austin2014">2014</a>)</span> and <span class="citation">Abadie and Spiess (<a href="#ref-abadie2019">2019</a>)</span>. The cluster bootstrap involves
sampling matched pairs/strata of units from the matched sample and
performing the analysis within each sample composed of the sampled
-pairs. <span class="citation">Abadie and Spiess (<a href="#ref-abadie2019" role="doc-biblioref">2019</a>)</span> derived
-analytically that the cluster bootstrap is valid for estimating SEs and
-confidence intervals in the same circumstances cluster robust SEs are;
-indeed, the cluster bootstrap SE is known to approximate the
-cluster-robust SE <span class="citation">(<a href="#ref-cameron2015" role="doc-biblioref">Cameron and Miller 2015</a>)</span>.</p>
+pairs. <span class="citation">Abadie and Spiess (<a href="#ref-abadie2019">2019</a>)</span> derived analytically that the
+cluster bootstrap is valid for estimating SEs and confidence intervals
+in the same circumstances cluster robust SEs are; indeed, the cluster
+bootstrap SE is known to approximate the cluster-robust SE <span class="citation">(<a href="#ref-cameron2015">Cameron and Miller
+2015</a>)</span>.</p>
<p>With bootstrapping, more bootstrap replications are always better but
can take time and increase the chances that at least one error will
occur within the bootstrap analysis (e.g., a bootstrap sample with zero
treated units or zero units with an event). In general, numbers of
replications upwards of 999 are recommended, with values one less than a
multiple of 100 preferred to avoid interpolation when using the
-percentiles as confidence interval limits <span class="citation">(<a href="#ref-mackinnon2006" role="doc-biblioref">MacKinnon
-2006</a>)</span>. There are several methods of computing bootstrap
-confidence intervals, but the bias-corrected accelerated (BCa) bootstrap
-confidence interval often performs best <span class="citation">(<a href="#ref-austin2014" role="doc-biblioref">Austin and Small 2014</a>;
-<a href="#ref-carpenter2000" role="doc-biblioref">Carpenter and Bithell
+percentiles as confidence interval limits <span class="citation">(<a href="#ref-mackinnon2006">MacKinnon 2006</a>)</span>. There are several
+methods of computing bootstrap confidence intervals, but the
+bias-corrected accelerated (BCa) bootstrap confidence interval often
+performs best <span class="citation">(<a href="#ref-austin2014">Austin
+and Small 2014</a>; <a href="#ref-carpenter2000">Carpenter and Bithell
2000</a>)</span> and is easy to implement, simply by setting
<code>type = "bca"</code> in the call to <code>boot::boot.ci()</code>
after running <code>boot::boot()</code><a href="#fn2" class="footnote-ref" id="fnref2"><sup>2</sup></a>.</p>
@@ -697,7 +697,7 @@ generate the dataset is at the end of this document. The focus here is
not on evaluating the methods but simply on demonstrating them. In all
cases, the correct propensity score model is used. Below we display the
first six rows of <code>d</code>:</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(d)</span></code></pre></div>
+<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">head</span>(d)</span></code></pre></div>
<pre><code>## A X1 X2 X3 X4 X5 X6 X7 X8 X9 Y_C Y_B Y_S
## 1 0 0.1725 -1.4283 -0.4103 -2.36059 1 -1.1199 0.6398 -0.4840 -0.59385 0.07104 0 278.46
## 2 0 -1.0959 0.8463 0.2456 -0.12333 1 -2.2687 -1.4491 -0.5514 -0.31439 0.15619 0 330.63
@@ -712,10 +712,10 @@ survival outcome.</p>
<p>We will need to the following packages to perform the desired
analyses:</p>
<ul>
-<li><code>marginaleffects</code> provides the <code>comparisons()</code>
-function for performing g-computation and estimating the SEs and
-confidence intervals of the average estimate potential outcomes and
-treatment effects</li>
+<li><code>marginaleffects</code> provides the
+<code>avg_comparisons()</code> function for performing g-computation and
+estimating the SEs and confidence intervals of the average estimate
+potential outcomes and treatment effects</li>
<li><code>sandwich</code> is used internally by
<code>marginaleffects</code> to compute robust and cluster-robust
SEs</li>
@@ -725,15 +725,15 @@ ratio, which we will use for survival outcomes.</li>
</ul>
<p>Of course, we also need <code>MatchIt</code> to perform the
matching.</p>
-<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"MatchIt"</span>)</span></code></pre></div>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"marginaleffects"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"MatchIt"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"marginaleffects"</span>)</span></code></pre></div>
<p>All effect estimates will be computed using
-<code>marginaleffects::comparions()</code>, even when its use may be
+<code>marginaleffects::avg_comparions()</code>, even when its use may be
superfluous (e.g., for performing a t-test in the matched set). As
previously mentioned, this is because it is useful to have a single
workflow that works no matter the situation, perhaps with very slight
modifications to accommodate different contexts. Using
-<code>comparisons()</code> has several advantages, even when the
+<code>avg_comparions()</code> has several advantages, even when the
alternatives are simple: it only provides the effect estimate, and not
other coefficients; it automatically incorporates robust and
cluster-robust SEs if requested; and it always produces average marginal
@@ -773,11 +773,11 @@ Bootstrapping to Estimate Confidence Intervals” below.</p>
ATT. Remember, all matching methods use this exact procedure or a slight
variation, so this section is critical even if you are using a different
matching method.</p>
-<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Optimal full matching on the PS for the ATT</span></span>
-<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>mF <span class="ot"><-</span> <span class="fu">matchit</span>(A <span class="sc">~</span> X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
-<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9, <span class="at">data =</span> d,</span>
-<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a> <span class="at">method =</span> <span class="st">"full"</span>, <span class="at">estimand =</span> <span class="st">"ATT"</span>)</span>
-<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a>mF</span></code></pre></div>
+<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="co">#Optimal full matching on the PS for the ATT</span></span>
+<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a>mF <span class="ot"><-</span> <span class="fu">matchit</span>(A <span class="sc">~</span> X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
+<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9, <span class="at">data =</span> d,</span>
+<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a> <span class="at">method =</span> <span class="st">"full"</span>, <span class="at">estimand =</span> <span class="st">"ATT"</span>)</span>
+<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a>mF</span></code></pre></div>
<pre><code>## A matchit object
## - method: Optimal full matching
## - distance: Propensity score
@@ -785,10 +785,10 @@ matching method.</p>
## - number of obs.: 2000 (original), 2000 (matched)
## - target estimand: ATT
## - covariates: X1, X2, X3, X4, X5, X6, X7, X8, X9</code></pre>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Extract matched data</span></span>
-<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>md <span class="ot"><-</span> <span class="fu">match.data</span>(mF)</span>
-<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(md)</span></code></pre></div>
+<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="co">#Extract matched data</span></span>
+<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a>md <span class="ot"><-</span> <span class="fu">match.data</span>(mF)</span>
+<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a></span>
+<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a><span class="fu">head</span>(md)</span></code></pre></div>
<pre><code>## A X1 X2 X3 X4 X5 X6 X7 X8 X9 Y_C Y_B Y_S distance weights subclass
## 1 0 0.1725 -1.4283 -0.4103 -2.36059 1 -1.1199 0.6398 -0.4840 -0.59385 0.07104 0 278.46 0.08461 0.35351 222
## 2 0 -1.0959 0.8463 0.2456 -0.12333 1 -2.2687 -1.4491 -0.5514 -0.31439 0.15619 0 330.63 0.01855 0.04652 112
@@ -812,26 +812,26 @@ always necessary, especially when excellent balance has been achieved.
You can also include the propensity score (usually labeled
<code>distance</code> in the <code>match.data()</code> output), which
can add some robustness, especially when modeled flexibly (e.g., with
-polynomial terms or splines) <span class="citation">(<a href="#ref-austinDoublePropensityscoreAdjustment2017" role="doc-biblioref">Austin 2017</a>)</span>; see <a href="https://stats.stackexchange.com/a/580174/116195">here</a> for an
+polynomial terms or splines) <span class="citation">(<a href="#ref-austinDoublePropensityscoreAdjustment2017">Austin
+2017</a>)</span>; see <a href="https://stats.stackexchange.com/a/580174/116195">here</a> for an
example.</p>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Linear model with covariates</span></span>
-<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>fit1 <span class="ot"><-</span> <span class="fu">lm</span>(Y_C <span class="sc">~</span> A <span class="sc">*</span> (X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
-<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9),</span>
-<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a> <span class="at">data =</span> md, <span class="at">weights =</span> weights)</span></code></pre></div>
-<p>Next, we use <code>marginaleffects::comparisons()</code> to estimate
-the ATT.</p>
-<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="fu">avg_comparisons</span>(fit1, <span class="at">variables =</span> <span class="st">"A"</span>,</span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a> <span class="at">vcov =</span> <span class="sc">~</span>subclass,</span>
-<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">subset</span>(md, A <span class="sc">==</span> <span class="dv">1</span>),</span>
-<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a> <span class="at">wts =</span> <span class="st">"weights"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="co">#Linear model with covariates</span></span>
+<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a>fit1 <span class="ot"><-</span> <span class="fu">lm</span>(Y_C <span class="sc">~</span> A <span class="sc">*</span> (X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
+<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9),</span>
+<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a> <span class="at">data =</span> md, <span class="at">weights =</span> weights)</span></code></pre></div>
+<p>Next, we use <code>marginaleffects::avg_comparisons()</code> to
+estimate the ATT.</p>
+<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a><span class="fu">avg_comparisons</span>(fit1, <span class="at">variables =</span> <span class="st">"A"</span>,</span>
+<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a> <span class="at">vcov =</span> <span class="sc">~</span>subclass,</span>
+<span id="cb10-3"><a href="#cb10-3" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">subset</span>(md, A <span class="sc">==</span> <span class="dv">1</span>),</span>
+<span id="cb10-4"><a href="#cb10-4" tabindex="-1"></a> <span class="at">wts =</span> <span class="st">"weights"</span>)</span></code></pre></div>
<pre><code>##
## Term Contrast Estimate Std. Error z Pr(>|z|) 2.5 % 97.5 %
-## A 1 - 0 1.71 0.496 3.44 0.00057 0.735 2.68
+## A 1 - 0 1.71 0.496 3.44 <0.001 0.735 2.68
##
-## Prediction type: response
-## Columns: type, term, contrast, estimate, std.error, statistic, p.value, conf.low, conf.high</code></pre>
+## Columns: term, contrast, estimate, std.error, statistic, p.value, conf.low, conf.high</code></pre>
<p>Let’s break down the call to <code>avg_comparisons()</code>: to the
-first argument, we supply the model fit, <code>fit</code>; to the
+first argument, we supply the model fit, <code>fit1</code>; to the
<code>variables</code> argument, the name of the treatment
(<code>"A"</code>); to the <code>vcov</code> argument, a formula with
subclass membership (<code>~subclass</code>) to request cluster-robust
@@ -849,18 +849,20 @@ potential outcomes, we can use
below. Note the interpretation of the resulting estimates as the
expected potential outcomes is only valid if all covariates present in
the outcome model (if any) are interacted with the treatment.</p>
-<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="fu">avg_predictions</span>(fit1, <span class="at">variables =</span> <span class="st">"A"</span>,</span>
-<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a> <span class="at">vcov =</span> <span class="sc">~</span>subclass,</span>
-<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">subset</span>(md, A <span class="sc">==</span> <span class="dv">1</span>),</span>
-<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a> <span class="at">wts =</span> <span class="st">"weights"</span>,</span>
-<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a> <span class="at">by =</span> <span class="st">"A"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a><span class="fu">avg_predictions</span>(fit1, <span class="at">variables =</span> <span class="st">"A"</span>,</span>
+<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a> <span class="at">vcov =</span> <span class="sc">~</span>subclass,</span>
+<span id="cb12-3"><a href="#cb12-3" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">subset</span>(md, A <span class="sc">==</span> <span class="dv">1</span>),</span>
+<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a> <span class="at">wts =</span> <span class="st">"weights"</span>)</span></code></pre></div>
+<pre><code>##
+## A Estimate Std. Error z Pr(>|z|) 2.5 % 97.5 %
+## 0 2.19 0.391 5.59 <0.001 1.42 2.95
+## 1 3.89 0.229 17.04 <0.001 3.45 4.34
+##
+## Columns: A, estimate, std.error, statistic, p.value, conf.low, conf.high</code></pre>
<p>We can see that the difference in potential outcome means is equal to
-the average treatment effect computed previously<a href="#fn4" class="footnote-ref" id="fnref4"><sup>4</sup></a>. Almost all of the
-arguments to <code>avg_predictions()</code> are the same as those to
-<code>avg_comparisons()</code>; the only difference is that the
-<code>by</code> argument needs to be specified naming the treatment.
-(Note: due to a bug, the above code will not evaluate unless
-<code>marginaleffects</code> is a version greater than 0.10.0.)</p>
+the average treatment effect computed previously<a href="#fn4" class="footnote-ref" id="fnref4"><sup>4</sup></a>. All of the arguments
+to <code>avg_predictions()</code> are the same as those to
+<code>avg_comparisons()</code>.</p>
</div>
<div id="adjustments-to-the-standard-case" class="section level3">
<h3>Adjustments to the Standard Case</h3>
@@ -882,28 +884,28 @@ treated units.</p>
<p>When matching with replacement (i.e., nearest neighbor or genetic
matching with <code>replace = TRUE</code>), effect and SE estimation
need to account for control unit multiplicity (i.e., repeated use) and
-within-pair correlations <span class="citation">(<a href="#ref-hill2006" role="doc-biblioref">Hill and Reiter 2006</a>; <a href="#ref-austin2020a" role="doc-biblioref">Austin and Cafri
-2020</a>)</span>. Although <span class="citation">Abadie and Imbens (<a href="#ref-abadie2008" role="doc-biblioref">2008</a>)</span>
-demonstrated analytically that bootstrap SEs may be invalid for matching
-with replacement, simulation work by <span class="citation">Hill and
-Reiter (<a href="#ref-hill2006" role="doc-biblioref">2006</a>)</span>
-and <span class="citation">Bodory et al. (<a href="#ref-bodory2020" role="doc-biblioref">2020</a>)</span> has found that bootstrap SEs are
-adequate and generally slightly conservative. See the section “Using
-Bootstrapping to Estimate Confidence Intervals” for instructions on
-using the bootstrap and an example that use matching with
-replacement.</p>
+within-pair correlations <span class="citation">(<a href="#ref-hill2006">Hill and Reiter 2006</a>; <a href="#ref-austin2020a">Austin and Cafri 2020</a>)</span>. Although
+<span class="citation">Abadie and Imbens (<a href="#ref-abadie2008">2008</a>)</span> demonstrated analytically that
+bootstrap SEs may be invalid for matching with replacement, simulation
+work by <span class="citation">Hill and Reiter (<a href="#ref-hill2006">2006</a>)</span> and <span class="citation">Bodory
+et al. (<a href="#ref-bodory2020">2020</a>)</span> has found that
+bootstrap SEs are adequate and generally slightly conservative. See the
+section “Using Bootstrapping to Estimate Confidence Intervals” for
+instructions on using the bootstrap and an example that use matching
+with replacement.</p>
<p>Because control units do not belong to unique pairs, there is no pair
membership in the <code>match.data()</code> output. One can simply
change <code>vcov = ~subclass</code> to <code>vcov = "HC3"</code> in the
calls to <code>comparisons()</code> and <code>predictions()</code> to
-use robust SEs instead of cluster-robust SEs, as recommended by <span class="citation">Hill and Reiter (<a href="#ref-hill2006" role="doc-biblioref">2006</a>)</span>. There is some evidence for an
+use robust SEs instead of cluster-robust SEs, as recommended by <span class="citation">Hill and Reiter (<a href="#ref-hill2006">2006</a>)</span>. There is some evidence for an
alternative approach that incorporates pair membership and adjusts for
reuse of control units, though this has only been studied for survival
-outcomes <span class="citation">(<a href="#ref-austin2020a" role="doc-biblioref">Austin and Cafri 2020</a>)</span>. This adjustment
-involves using two-way cluster-robust SEs with pair membership and unit
-ID as the clustering variables. For continuous and binary outcomes, this
-involves the following two changes: 1) replace <code>match.data()</code>
-with <code>get_matches()</code>, which produces a dataset with a row per
+outcomes <span class="citation">(<a href="#ref-austin2020a">Austin and
+Cafri 2020</a>)</span>. This adjustment involves using two-way
+cluster-robust SEs with pair membership and unit ID as the clustering
+variables. For continuous and binary outcomes, this involves the
+following two changes: 1) replace <code>match.data()</code> with
+<code>get_matches()</code>, which produces a dataset with one row per
unit per pair, meaning control units matched to multiple treated units
will appear multiple times in the dataset; 2) set
<code>vcov = ~subclass + id</code> in the calls to
@@ -932,8 +934,9 @@ effects and pool them using an average marginal effects procedure, and
the second is to use the stratum weights to estimate a single average
marginal effect. This latter approach is also known as marginal mean
weighting through stratification (MMWS), and is described in detail by
-<span class="citation">Hong (<a href="#ref-hong2010" role="doc-biblioref">2010</a>)</span><a href="#fn5" class="footnote-ref" id="fnref5"><sup>5</sup></a>. When done properly, both methods should
-yield similar or identical estimates of the treatment effect.</p>
+<span class="citation">Hong (<a href="#ref-hong2010">2010</a>)</span><a href="#fn5" class="footnote-ref" id="fnref5"><sup>5</sup></a>. When done
+properly, both methods should yield similar or identical estimates of
+the treatment effect.</p>
<p>All of the methods described above for the Standard Case also work
with MMWS because the formation of the weights is the same; the only
difference is that it is not appropriate to use cluster-robust SEs with
@@ -954,28 +957,27 @@ and the <code>weights</code> argument should be omitted. In the calls to
change <code>vcov = ~subclass</code> to <code>vcov = "HC3"</code> in the
calls to <code>avg_comparisons()</code> and
<code>avg_predictions()</code>. See an example below:</p>
-<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Subclassification on the PS for the ATT</span></span>
-<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>mS <span class="ot"><-</span> <span class="fu">matchit</span>(A <span class="sc">~</span> X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
-<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9, <span class="at">data =</span> d,</span>
-<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a> <span class="at">method =</span> <span class="st">"subclass"</span>, <span class="at">estimand =</span> <span class="st">"ATT"</span>)</span>
-<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a><span class="co">#Extract matched data</span></span>
-<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a>md <span class="ot"><-</span> <span class="fu">match.data</span>(mS)</span>
-<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a>fitS <span class="ot"><-</span> <span class="fu">lm</span>(Y_C <span class="sc">~</span> subclass <span class="sc">*</span> (A <span class="sc">*</span> (X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
-<span id="cb13-10"><a href="#cb13-10" aria-hidden="true" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9)),</span>
-<span id="cb13-11"><a href="#cb13-11" aria-hidden="true" tabindex="-1"></a> <span class="at">data =</span> md)</span>
-<span id="cb13-12"><a href="#cb13-12" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb13-13"><a href="#cb13-13" aria-hidden="true" tabindex="-1"></a><span class="fu">avg_comparisons</span>(fitS,</span>
-<span id="cb13-14"><a href="#cb13-14" aria-hidden="true" tabindex="-1"></a> <span class="at">variables =</span> <span class="st">"A"</span>,</span>
-<span id="cb13-15"><a href="#cb13-15" aria-hidden="true" tabindex="-1"></a> <span class="at">vcov =</span> <span class="st">"HC3"</span>,</span>
-<span id="cb13-16"><a href="#cb13-16" aria-hidden="true" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">subset</span>(md, A <span class="sc">==</span> <span class="dv">1</span>))</span></code></pre></div>
+<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a><span class="co">#Subclassification on the PS for the ATT</span></span>
+<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a>mS <span class="ot"><-</span> <span class="fu">matchit</span>(A <span class="sc">~</span> X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
+<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9, <span class="at">data =</span> d,</span>
+<span id="cb14-4"><a href="#cb14-4" tabindex="-1"></a> <span class="at">method =</span> <span class="st">"subclass"</span>, <span class="at">estimand =</span> <span class="st">"ATT"</span>)</span>
+<span id="cb14-5"><a href="#cb14-5" tabindex="-1"></a></span>
+<span id="cb14-6"><a href="#cb14-6" tabindex="-1"></a><span class="co">#Extract matched data</span></span>
+<span id="cb14-7"><a href="#cb14-7" tabindex="-1"></a>md <span class="ot"><-</span> <span class="fu">match.data</span>(mS)</span>
+<span id="cb14-8"><a href="#cb14-8" tabindex="-1"></a></span>
+<span id="cb14-9"><a href="#cb14-9" tabindex="-1"></a>fitS <span class="ot"><-</span> <span class="fu">lm</span>(Y_C <span class="sc">~</span> subclass <span class="sc">*</span> (A <span class="sc">*</span> (X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
+<span id="cb14-10"><a href="#cb14-10" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9)),</span>
+<span id="cb14-11"><a href="#cb14-11" tabindex="-1"></a> <span class="at">data =</span> md)</span>
+<span id="cb14-12"><a href="#cb14-12" tabindex="-1"></a></span>
+<span id="cb14-13"><a href="#cb14-13" tabindex="-1"></a><span class="fu">avg_comparisons</span>(fitS,</span>
+<span id="cb14-14"><a href="#cb14-14" tabindex="-1"></a> <span class="at">variables =</span> <span class="st">"A"</span>,</span>
+<span id="cb14-15"><a href="#cb14-15" tabindex="-1"></a> <span class="at">vcov =</span> <span class="st">"HC3"</span>,</span>
+<span id="cb14-16"><a href="#cb14-16" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">subset</span>(md, A <span class="sc">==</span> <span class="dv">1</span>))</span></code></pre></div>
<pre><code>##
## Term Contrast Estimate Std. Error z Pr(>|z|) 2.5 % 97.5 %
-## A 1 - 0 1.65 0.364 4.54 5.6e-06 0.94 2.37
+## A 1 - 0 1.65 0.364 4.54 <0.001 0.94 2.37
##
-## Prediction type: response
-## Columns: type, term, contrast, estimate, std.error, statistic, p.value, conf.low, conf.high</code></pre>
+## Columns: term, contrast, estimate, std.error, statistic, p.value, conf.low, conf.high</code></pre>
<p>A model with fewer terms may be required when subclasses are small;
removing covariates or their interactions with treatment may be required
and can increase precision in smaller datasets. Remember that if
@@ -998,42 +1000,38 @@ measure because we can compute any of the above effect measures using
compute the marginal RD, we can use exactly the same syntax as in the
Standard Case; nothing needs to change<a href="#fn7" class="footnote-ref" id="fnref7"><sup>7</sup></a>.</p>
<p>To compute the marginal RR, we need to add
-<code>transform_pre = "lnratioavg"</code> to
+<code>comparison = "lnratioavg"</code> to
<code>avg_comparisons()</code>; this computes the marginal log RR. To
-get the marginal RR, we need to add <code>transform_post = "exp"</code>
-to <code>avg_comparisons()</code>, which exponentiates the marginal log
-RR and its confidence interval. The code below computes the effects and
+get the marginal RR, we need to add <code>transform = "exp"</code> to
+<code>avg_comparisons()</code>, which exponentiates the marginal log RR
+and its confidence interval. The code below computes the effects and
displays the statistics of interest:</p>
-<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Logistic regression model with covariates</span></span>
-<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>fit2 <span class="ot"><-</span> <span class="fu">glm</span>(Y_B <span class="sc">~</span> A <span class="sc">*</span> (X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
-<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9),</span>
-<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a> <span class="at">data =</span> md, <span class="at">weights =</span> weights,</span>
-<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a> <span class="at">family =</span> <span class="fu">quasibinomial</span>())</span>
-<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a><span class="co">#Compute effects; RR and confidence interval</span></span>
-<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a><span class="fu">avg_comparisons</span>(fit2,</span>
-<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a> <span class="at">variables =</span> <span class="st">"A"</span>,</span>
-<span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a> <span class="at">vcov =</span> <span class="sc">~</span>subclass,</span>
-<span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">subset</span>(md, A <span class="sc">==</span> <span class="dv">1</span>),</span>
-<span id="cb15-12"><a href="#cb15-12" aria-hidden="true" tabindex="-1"></a> <span class="at">wts =</span> <span class="st">"weights"</span>,</span>
-<span id="cb15-13"><a href="#cb15-13" aria-hidden="true" tabindex="-1"></a> <span class="at">transform_pre =</span> <span class="st">"lnratioavg"</span>,</span>
-<span id="cb15-14"><a href="#cb15-14" aria-hidden="true" tabindex="-1"></a> <span class="at">transform_post =</span> <span class="st">"exp"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a><span class="co">#Logistic regression model with covariates</span></span>
+<span id="cb16-2"><a href="#cb16-2" tabindex="-1"></a>fit2 <span class="ot"><-</span> <span class="fu">glm</span>(Y_B <span class="sc">~</span> A <span class="sc">*</span> (X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
+<span id="cb16-3"><a href="#cb16-3" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9),</span>
+<span id="cb16-4"><a href="#cb16-4" tabindex="-1"></a> <span class="at">data =</span> md, <span class="at">weights =</span> weights,</span>
+<span id="cb16-5"><a href="#cb16-5" tabindex="-1"></a> <span class="at">family =</span> <span class="fu">quasibinomial</span>())</span>
+<span id="cb16-6"><a href="#cb16-6" tabindex="-1"></a></span>
+<span id="cb16-7"><a href="#cb16-7" tabindex="-1"></a><span class="co">#Compute effects; RR and confidence interval</span></span>
+<span id="cb16-8"><a href="#cb16-8" tabindex="-1"></a><span class="fu">avg_comparisons</span>(fit2,</span>
+<span id="cb16-9"><a href="#cb16-9" tabindex="-1"></a> <span class="at">variables =</span> <span class="st">"A"</span>,</span>
+<span id="cb16-10"><a href="#cb16-10" tabindex="-1"></a> <span class="at">vcov =</span> <span class="sc">~</span>subclass,</span>
+<span id="cb16-11"><a href="#cb16-11" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">subset</span>(md, A <span class="sc">==</span> <span class="dv">1</span>),</span>
+<span id="cb16-12"><a href="#cb16-12" tabindex="-1"></a> <span class="at">wts =</span> <span class="st">"weights"</span>,</span>
+<span id="cb16-13"><a href="#cb16-13" tabindex="-1"></a> <span class="at">comparison =</span> <span class="st">"lnratioavg"</span>,</span>
+<span id="cb16-14"><a href="#cb16-14" tabindex="-1"></a> <span class="at">transform =</span> <span class="st">"exp"</span>)</span></code></pre></div>
<pre><code>##
## Term Contrast Estimate Pr(>|z|) 2.5 % 97.5 %
-## A ln(mean(1) / mean(0)) 1.57 <2e-16 1.41 1.75
+## A ln(mean(1) / mean(0)) 1.57 <0.001 1.41 1.75
##
-## Prediction type: response
-## Post-transformation: exp
-## Columns: type, term, contrast, estimate, p.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo</code></pre>
+## Columns: term, contrast, estimate, p.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo</code></pre>
<p>The output displays the marginal RR, its Z-value, the p-value for the
-Z-test against 0, and its confidence interval. (Note that even though
-the <code>Contrast</code> label still suggests the log risk ratio, the
-risk ratio is actually displayed. You can confirm this by manually
-exponentiating the log risk ratio computed just before.) To view the log
-RR and its standard error, omit the <code>transform_post</code>
-argument.</p>
+Z-test of the log RR against 0, and its confidence interval. (Note that
+even though the <code>Contrast</code> label still suggests the log RR,
+the RR is actually displayed.) To view the log RR and its standard
+error, omit the <code>transform</code> argument.</p>
<p>For the marginal OR, the only thing that needs to change is that
-<code>transform_pre</code> should be set to <code>"lnoravg"</code>.</p>
+<code>comparison</code> should be set to <code>"lnoravg"</code>.</p>
</div>
<div id="survival-outcomes" class="section level4">
<h4>Survival outcomes</h4>
@@ -1052,18 +1050,18 @@ means that we cannot use the procedures from the Standard Case. Here we
describe estimating the marginal HR using <code>coxph()</code> from the
<code>survival</code> package. (See
<code>help("coxph", package = "survival")</code> for more information on
-this model.) To request cluster-robust SEs as recommended by <span class="citation">Austin (<a href="#ref-austin2013a" role="doc-biblioref">2013b</a>)</span>, we need to supply pair
-membership (stored in the <code>subclass</code> column of
-<code>md</code>) to the <code>cluster</code> argument and set
+this model.) To request cluster-robust SEs as recommended by <span class="citation">Austin (<a href="#ref-austin2013a">2013b</a>)</span>,
+we need to supply pair membership (stored in the <code>subclass</code>
+column of <code>md</code>) to the <code>cluster</code> argument and set
<code>robust = TRUE</code>. For matching methods that don’t involve
pairing (e.g., cardinality and template matching and [coarsened] exact
matching), we can omit the <code>cluster</code> argument (but keep
<code>robust = TRUE</code>)<a href="#fn9" class="footnote-ref" id="fnref9"><sup>9</sup></a>.</p>
-<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"survival"</span>)</span>
-<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a><span class="co">#Cox Regression for marginal HR</span></span>
-<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a><span class="fu">coxph</span>(<span class="fu">Surv</span>(Y_S) <span class="sc">~</span> A, <span class="at">data =</span> md, <span class="at">robust =</span> <span class="cn">TRUE</span>, </span>
-<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a> <span class="at">weights =</span> weights, <span class="at">cluster =</span> subclass)</span></code></pre></div>
+<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"survival"</span>)</span>
+<span id="cb18-2"><a href="#cb18-2" tabindex="-1"></a></span>
+<span id="cb18-3"><a href="#cb18-3" tabindex="-1"></a><span class="co">#Cox Regression for marginal HR</span></span>
+<span id="cb18-4"><a href="#cb18-4" tabindex="-1"></a><span class="fu">coxph</span>(<span class="fu">Surv</span>(Y_S) <span class="sc">~</span> A, <span class="at">data =</span> md, <span class="at">robust =</span> <span class="cn">TRUE</span>, </span>
+<span id="cb18-5"><a href="#cb18-5" tabindex="-1"></a> <span class="at">weights =</span> weights, <span class="at">cluster =</span> subclass)</span></code></pre></div>
<pre><code>## Call:
## coxph(formula = Surv(Y_S) ~ A, data = md, weights = weights,
## robust = TRUE, cluster = subclass)
@@ -1077,7 +1075,7 @@ matching), we can omit the <code>cluster</code> argument (but keep
<code>exp(coef)</code> contains the HR. Remember to always use the
<code>robust se</code> for the SE of the log HR. The displayed z-test
p-value results from using the robust SE.</p>
-<p>For matching with replacement, a special procedure described by <span class="citation">Austin and Cafri (<a href="#ref-austin2020a" role="doc-biblioref">2020</a>)</span> can be necessary for valid
+<p>For matching with replacement, a special procedure described by <span class="citation">Austin and Cafri (<a href="#ref-austin2020a">2020</a>)</span> can be necessary for valid
inference. According to the results of their simulation studies, when
the treatment prevalence is low (<30%), a SE that does not involve
pair membership (i.e., the <code>match.data()</code> approach, as
@@ -1088,31 +1086,31 @@ multiplicity and pairing.</p>
<p>Doing so must be done manually for survival models using
<code>get_matches()</code> and several calls to <code>coxph()</code> as
demonstrated in the appendix of <span class="citation">Austin and Cafri
-(<a href="#ref-austin2020a" role="doc-biblioref">2020</a>)</span>. We
-demonstrate this below:</p>
-<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="co">#get_matches() after matching with replacement</span></span>
-<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>gm <span class="ot"><-</span> <span class="fu">get_matches</span>(mR)</span>
-<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a><span class="co">#Austin & Cafri's (2020) SE estimator</span></span>
-<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a>fs <span class="ot"><-</span> <span class="fu">coxph</span>(<span class="fu">Surv</span>(Y_S) <span class="sc">~</span> A, <span class="at">data =</span> gm, <span class="at">robust =</span> <span class="cn">TRUE</span>, </span>
-<span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a> <span class="at">weights =</span> weights, <span class="at">cluster =</span> subclass)</span>
-<span id="cb19-7"><a href="#cb19-7" aria-hidden="true" tabindex="-1"></a>Vs <span class="ot"><-</span> fs<span class="sc">$</span>var</span>
-<span id="cb19-8"><a href="#cb19-8" aria-hidden="true" tabindex="-1"></a>ks <span class="ot"><-</span> <span class="fu">nlevels</span>(gm<span class="sc">$</span>subclass)</span>
-<span id="cb19-9"><a href="#cb19-9" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb19-10"><a href="#cb19-10" aria-hidden="true" tabindex="-1"></a>fi <span class="ot"><-</span> <span class="fu">coxph</span>(<span class="fu">Surv</span>(Y_S) <span class="sc">~</span> A, <span class="at">data =</span> gm, <span class="at">robust =</span> <span class="cn">TRUE</span>, </span>
-<span id="cb19-11"><a href="#cb19-11" aria-hidden="true" tabindex="-1"></a> <span class="at">weights =</span> weights, <span class="at">cluster =</span> id)</span>
-<span id="cb19-12"><a href="#cb19-12" aria-hidden="true" tabindex="-1"></a>Vi <span class="ot"><-</span> fi<span class="sc">$</span>var</span>
-<span id="cb19-13"><a href="#cb19-13" aria-hidden="true" tabindex="-1"></a>ki <span class="ot"><-</span> <span class="fu">length</span>(<span class="fu">unique</span>(gm<span class="sc">$</span>id))</span>
-<span id="cb19-14"><a href="#cb19-14" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb19-15"><a href="#cb19-15" aria-hidden="true" tabindex="-1"></a>fc <span class="ot"><-</span> <span class="fu">coxph</span>(<span class="fu">Surv</span>(Y_S) <span class="sc">~</span> A, <span class="at">data =</span> gm, <span class="at">robust =</span> <span class="cn">TRUE</span>, </span>
-<span id="cb19-16"><a href="#cb19-16" aria-hidden="true" tabindex="-1"></a> <span class="at">weights =</span> weights)</span>
-<span id="cb19-17"><a href="#cb19-17" aria-hidden="true" tabindex="-1"></a>Vc <span class="ot"><-</span> fc<span class="sc">$</span>var</span>
-<span id="cb19-18"><a href="#cb19-18" aria-hidden="true" tabindex="-1"></a>kc <span class="ot"><-</span> <span class="fu">nrow</span>(gm)</span>
-<span id="cb19-19"><a href="#cb19-19" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb19-20"><a href="#cb19-20" aria-hidden="true" tabindex="-1"></a><span class="co">#Compute the variance and sneak it back into the fit object</span></span>
-<span id="cb19-21"><a href="#cb19-21" aria-hidden="true" tabindex="-1"></a>fc<span class="sc">$</span>var <span class="ot"><-</span> (ks<span class="sc">/</span>(ks<span class="dv">-1</span>))<span class="sc">*</span>Vs <span class="sc">+</span> (ki<span class="sc">/</span>(ki<span class="dv">-1</span>))<span class="sc">*</span>Vi <span class="sc">-</span> (kc<span class="sc">/</span>(kc<span class="dv">-1</span>))<span class="sc">*</span>Vc</span>
-<span id="cb19-22"><a href="#cb19-22" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb19-23"><a href="#cb19-23" aria-hidden="true" tabindex="-1"></a>fc</span></code></pre></div>
+(<a href="#ref-austin2020a">2020</a>)</span>. We demonstrate this
+below:</p>
+<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" tabindex="-1"></a><span class="co">#get_matches() after matching with replacement</span></span>
+<span id="cb20-2"><a href="#cb20-2" tabindex="-1"></a>gm <span class="ot"><-</span> <span class="fu">get_matches</span>(mR)</span>
+<span id="cb20-3"><a href="#cb20-3" tabindex="-1"></a></span>
+<span id="cb20-4"><a href="#cb20-4" tabindex="-1"></a><span class="co">#Austin & Cafri's (2020) SE estimator</span></span>
+<span id="cb20-5"><a href="#cb20-5" tabindex="-1"></a>fs <span class="ot"><-</span> <span class="fu">coxph</span>(<span class="fu">Surv</span>(Y_S) <span class="sc">~</span> A, <span class="at">data =</span> gm, <span class="at">robust =</span> <span class="cn">TRUE</span>, </span>
+<span id="cb20-6"><a href="#cb20-6" tabindex="-1"></a> <span class="at">weights =</span> weights, <span class="at">cluster =</span> subclass)</span>
+<span id="cb20-7"><a href="#cb20-7" tabindex="-1"></a>Vs <span class="ot"><-</span> fs<span class="sc">$</span>var</span>
+<span id="cb20-8"><a href="#cb20-8" tabindex="-1"></a>ks <span class="ot"><-</span> <span class="fu">nlevels</span>(gm<span class="sc">$</span>subclass)</span>
+<span id="cb20-9"><a href="#cb20-9" tabindex="-1"></a></span>
+<span id="cb20-10"><a href="#cb20-10" tabindex="-1"></a>fi <span class="ot"><-</span> <span class="fu">coxph</span>(<span class="fu">Surv</span>(Y_S) <span class="sc">~</span> A, <span class="at">data =</span> gm, <span class="at">robust =</span> <span class="cn">TRUE</span>, </span>
+<span id="cb20-11"><a href="#cb20-11" tabindex="-1"></a> <span class="at">weights =</span> weights, <span class="at">cluster =</span> id)</span>
+<span id="cb20-12"><a href="#cb20-12" tabindex="-1"></a>Vi <span class="ot"><-</span> fi<span class="sc">$</span>var</span>
+<span id="cb20-13"><a href="#cb20-13" tabindex="-1"></a>ki <span class="ot"><-</span> <span class="fu">length</span>(<span class="fu">unique</span>(gm<span class="sc">$</span>id))</span>
+<span id="cb20-14"><a href="#cb20-14" tabindex="-1"></a></span>
+<span id="cb20-15"><a href="#cb20-15" tabindex="-1"></a>fc <span class="ot"><-</span> <span class="fu">coxph</span>(<span class="fu">Surv</span>(Y_S) <span class="sc">~</span> A, <span class="at">data =</span> gm, <span class="at">robust =</span> <span class="cn">TRUE</span>, </span>
+<span id="cb20-16"><a href="#cb20-16" tabindex="-1"></a> <span class="at">weights =</span> weights)</span>
+<span id="cb20-17"><a href="#cb20-17" tabindex="-1"></a>Vc <span class="ot"><-</span> fc<span class="sc">$</span>var</span>
+<span id="cb20-18"><a href="#cb20-18" tabindex="-1"></a>kc <span class="ot"><-</span> <span class="fu">nrow</span>(gm)</span>
+<span id="cb20-19"><a href="#cb20-19" tabindex="-1"></a></span>
+<span id="cb20-20"><a href="#cb20-20" tabindex="-1"></a><span class="co">#Compute the variance and sneak it back into the fit object</span></span>
+<span id="cb20-21"><a href="#cb20-21" tabindex="-1"></a>fc<span class="sc">$</span>var <span class="ot"><-</span> (ks<span class="sc">/</span>(ks<span class="dv">-1</span>))<span class="sc">*</span>Vs <span class="sc">+</span> (ki<span class="sc">/</span>(ki<span class="dv">-1</span>))<span class="sc">*</span>Vi <span class="sc">-</span> (kc<span class="sc">/</span>(kc<span class="dv">-1</span>))<span class="sc">*</span>Vc</span>
+<span id="cb20-22"><a href="#cb20-22" tabindex="-1"></a></span>
+<span id="cb20-23"><a href="#cb20-23" tabindex="-1"></a>fc</span></code></pre></div>
<p>The <code>robust se</code> column contains the computed SE, and the
reported Z-test uses this SE. The <code>se(coef)</code> column should be
ignored.</p>
@@ -1138,53 +1136,53 @@ estimated quantity of interest. This function should perform the
matching on the bootstrap sample, fit the outcome model, and estimate
the treatment effect using g-computation. In this example, we’ll use
matching with replacement, since the standard bootstrap has been found
-to work well with it <span class="citation">(<a href="#ref-bodory2020" role="doc-biblioref">Bodory et al. 2020</a>; <a href="#ref-hill2006" role="doc-biblioref">Hill and Reiter 2006</a>)</span>, despite some
-analytic results recommending otherwise <span class="citation">(<a href="#ref-abadie2008" role="doc-biblioref">Abadie and Imbens
-2008</a>)</span>. We’ll implement g-computation manually rather than
-using <code>avg_comparisons()</code>, as this dramatically improves the
-speed of the estimation since we don’t require standard errors to be
-estimated in each sample (or other processing
-<code>avg_comparisons()</code> does). We’ll consider the marginal RR ATT
-of <code>A</code> on the binary outcome <code>Y_B</code>.</p>
-<p>The first step is to write the estimation function, we we call
+to work well with it <span class="citation">(<a href="#ref-bodory2020">Bodory et al. 2020</a>; <a href="#ref-hill2006">Hill and Reiter 2006</a>)</span>, despite some
+analytic results recommending otherwise <span class="citation">(<a href="#ref-abadie2008">Abadie and Imbens 2008</a>)</span>. We’ll
+implement g-computation manually rather than using
+<code>avg_comparisons()</code>, as this dramatically improves the speed
+of the estimation since we don’t require standard errors to be estimated
+in each sample (or other processing <code>avg_comparisons()</code>
+does). We’ll consider the marginal RR ATT of <code>A</code> on the
+binary outcome <code>Y_B</code>.</p>
+<p>The first step is to write the estimation function, we call
<code>boot_fun</code>. This function returns the marginal RR. In it, we
perform the matching, estimate the effect, and return the estimate of
interest.</p>
-<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>boot_fun <span class="ot"><-</span> <span class="cf">function</span>(data, i) {</span>
-<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a> boot_data <span class="ot"><-</span> data[i,]</span>
-<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a> <span class="co">#Do 1:1 PS matching with replacement</span></span>
-<span id="cb20-5"><a href="#cb20-5" aria-hidden="true" tabindex="-1"></a> m <span class="ot"><-</span> <span class="fu">matchit</span>(A <span class="sc">~</span> X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
-<span id="cb20-6"><a href="#cb20-6" aria-hidden="true" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9,</span>
-<span id="cb20-7"><a href="#cb20-7" aria-hidden="true" tabindex="-1"></a> <span class="at">data =</span> boot_data,</span>
-<span id="cb20-8"><a href="#cb20-8" aria-hidden="true" tabindex="-1"></a> <span class="at">replace =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb20-9"><a href="#cb20-9" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb20-10"><a href="#cb20-10" aria-hidden="true" tabindex="-1"></a> <span class="co">#Extract matched dataset</span></span>
-<span id="cb20-11"><a href="#cb20-11" aria-hidden="true" tabindex="-1"></a> md <span class="ot"><-</span> <span class="fu">match.data</span>(m, <span class="at">data =</span> boot_data)</span>
-<span id="cb20-12"><a href="#cb20-12" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb20-13"><a href="#cb20-13" aria-hidden="true" tabindex="-1"></a> <span class="co">#Fit outcome model</span></span>
-<span id="cb20-14"><a href="#cb20-14" aria-hidden="true" tabindex="-1"></a> fit <span class="ot"><-</span> <span class="fu">glm</span>(Y_B <span class="sc">~</span> A <span class="sc">*</span> (X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
-<span id="cb20-15"><a href="#cb20-15" aria-hidden="true" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9),</span>
-<span id="cb20-16"><a href="#cb20-16" aria-hidden="true" tabindex="-1"></a> <span class="at">data =</span> md, <span class="at">weights =</span> weights,</span>
-<span id="cb20-17"><a href="#cb20-17" aria-hidden="true" tabindex="-1"></a> <span class="at">family =</span> <span class="fu">quasibinomial</span>())</span>
-<span id="cb20-18"><a href="#cb20-18" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb20-19"><a href="#cb20-19" aria-hidden="true" tabindex="-1"></a> <span class="do">## G-computation ##</span></span>
-<span id="cb20-20"><a href="#cb20-20" aria-hidden="true" tabindex="-1"></a> <span class="co">#Subset to treated units for ATT; skip for ATE</span></span>
-<span id="cb20-21"><a href="#cb20-21" aria-hidden="true" tabindex="-1"></a> md1 <span class="ot"><-</span> <span class="fu">subset</span>(md, A <span class="sc">==</span> <span class="dv">1</span>)</span>
-<span id="cb20-22"><a href="#cb20-22" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb20-23"><a href="#cb20-23" aria-hidden="true" tabindex="-1"></a> <span class="co">#Estimated potential outcomes under treatment</span></span>
-<span id="cb20-24"><a href="#cb20-24" aria-hidden="true" tabindex="-1"></a> p1 <span class="ot"><-</span> <span class="fu">predict</span>(fit, <span class="at">type =</span> <span class="st">"response"</span>,</span>
-<span id="cb20-25"><a href="#cb20-25" aria-hidden="true" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">transform</span>(md1, <span class="at">A =</span> <span class="dv">1</span>))</span>
-<span id="cb20-26"><a href="#cb20-26" aria-hidden="true" tabindex="-1"></a> Ep1 <span class="ot"><-</span> <span class="fu">weighted.mean</span>(p1, md1<span class="sc">$</span>weights)</span>
-<span id="cb20-27"><a href="#cb20-27" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb20-28"><a href="#cb20-28" aria-hidden="true" tabindex="-1"></a> <span class="co">#Estimated potential outcomes under control</span></span>
-<span id="cb20-29"><a href="#cb20-29" aria-hidden="true" tabindex="-1"></a> p0 <span class="ot"><-</span> <span class="fu">predict</span>(fit, <span class="at">type =</span> <span class="st">"response"</span>,</span>
-<span id="cb20-30"><a href="#cb20-30" aria-hidden="true" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">transform</span>(md1, <span class="at">A =</span> <span class="dv">0</span>))</span>
-<span id="cb20-31"><a href="#cb20-31" aria-hidden="true" tabindex="-1"></a> Ep0 <span class="ot"><-</span> <span class="fu">weighted.mean</span>(p0, md1<span class="sc">$</span>weights)</span>
-<span id="cb20-32"><a href="#cb20-32" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb20-33"><a href="#cb20-33" aria-hidden="true" tabindex="-1"></a> <span class="co">#Risk ratio</span></span>
-<span id="cb20-34"><a href="#cb20-34" aria-hidden="true" tabindex="-1"></a> <span class="fu">return</span>(Ep1 <span class="sc">/</span> Ep0)</span>
-<span id="cb20-35"><a href="#cb20-35" aria-hidden="true" tabindex="-1"></a>}</span></code></pre></div>
+<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a>boot_fun <span class="ot"><-</span> <span class="cf">function</span>(data, i) {</span>
+<span id="cb21-2"><a href="#cb21-2" tabindex="-1"></a> boot_data <span class="ot"><-</span> data[i,]</span>
+<span id="cb21-3"><a href="#cb21-3" tabindex="-1"></a> </span>
+<span id="cb21-4"><a href="#cb21-4" tabindex="-1"></a> <span class="co">#Do 1:1 PS matching with replacement</span></span>
+<span id="cb21-5"><a href="#cb21-5" tabindex="-1"></a> m <span class="ot"><-</span> <span class="fu">matchit</span>(A <span class="sc">~</span> X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
+<span id="cb21-6"><a href="#cb21-6" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9,</span>
+<span id="cb21-7"><a href="#cb21-7" tabindex="-1"></a> <span class="at">data =</span> boot_data,</span>
+<span id="cb21-8"><a href="#cb21-8" tabindex="-1"></a> <span class="at">replace =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb21-9"><a href="#cb21-9" tabindex="-1"></a> </span>
+<span id="cb21-10"><a href="#cb21-10" tabindex="-1"></a> <span class="co">#Extract matched dataset</span></span>
+<span id="cb21-11"><a href="#cb21-11" tabindex="-1"></a> md <span class="ot"><-</span> <span class="fu">match.data</span>(m, <span class="at">data =</span> boot_data)</span>
+<span id="cb21-12"><a href="#cb21-12" tabindex="-1"></a> </span>
+<span id="cb21-13"><a href="#cb21-13" tabindex="-1"></a> <span class="co">#Fit outcome model</span></span>
+<span id="cb21-14"><a href="#cb21-14" tabindex="-1"></a> fit <span class="ot"><-</span> <span class="fu">glm</span>(Y_B <span class="sc">~</span> A <span class="sc">*</span> (X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
+<span id="cb21-15"><a href="#cb21-15" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9),</span>
+<span id="cb21-16"><a href="#cb21-16" tabindex="-1"></a> <span class="at">data =</span> md, <span class="at">weights =</span> weights,</span>
+<span id="cb21-17"><a href="#cb21-17" tabindex="-1"></a> <span class="at">family =</span> <span class="fu">quasibinomial</span>())</span>
+<span id="cb21-18"><a href="#cb21-18" tabindex="-1"></a> </span>
+<span id="cb21-19"><a href="#cb21-19" tabindex="-1"></a> <span class="do">## G-computation ##</span></span>
+<span id="cb21-20"><a href="#cb21-20" tabindex="-1"></a> <span class="co">#Subset to treated units for ATT; skip for ATE</span></span>
+<span id="cb21-21"><a href="#cb21-21" tabindex="-1"></a> md1 <span class="ot"><-</span> <span class="fu">subset</span>(md, A <span class="sc">==</span> <span class="dv">1</span>)</span>
+<span id="cb21-22"><a href="#cb21-22" tabindex="-1"></a> </span>
+<span id="cb21-23"><a href="#cb21-23" tabindex="-1"></a> <span class="co">#Estimated potential outcomes under treatment</span></span>
+<span id="cb21-24"><a href="#cb21-24" tabindex="-1"></a> p1 <span class="ot"><-</span> <span class="fu">predict</span>(fit, <span class="at">type =</span> <span class="st">"response"</span>,</span>
+<span id="cb21-25"><a href="#cb21-25" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">transform</span>(md1, <span class="at">A =</span> <span class="dv">1</span>))</span>
+<span id="cb21-26"><a href="#cb21-26" tabindex="-1"></a> Ep1 <span class="ot"><-</span> <span class="fu">weighted.mean</span>(p1, md1<span class="sc">$</span>weights)</span>
+<span id="cb21-27"><a href="#cb21-27" tabindex="-1"></a> </span>
+<span id="cb21-28"><a href="#cb21-28" tabindex="-1"></a> <span class="co">#Estimated potential outcomes under control</span></span>
+<span id="cb21-29"><a href="#cb21-29" tabindex="-1"></a> p0 <span class="ot"><-</span> <span class="fu">predict</span>(fit, <span class="at">type =</span> <span class="st">"response"</span>,</span>
+<span id="cb21-30"><a href="#cb21-30" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">transform</span>(md1, <span class="at">A =</span> <span class="dv">0</span>))</span>
+<span id="cb21-31"><a href="#cb21-31" tabindex="-1"></a> Ep0 <span class="ot"><-</span> <span class="fu">weighted.mean</span>(p0, md1<span class="sc">$</span>weights)</span>
+<span id="cb21-32"><a href="#cb21-32" tabindex="-1"></a> </span>
+<span id="cb21-33"><a href="#cb21-33" tabindex="-1"></a> <span class="co">#Risk ratio</span></span>
+<span id="cb21-34"><a href="#cb21-34" tabindex="-1"></a> <span class="fu">return</span>(Ep1 <span class="sc">/</span> Ep0)</span>
+<span id="cb21-35"><a href="#cb21-35" tabindex="-1"></a>}</span></code></pre></div>
<p>Next, we call <code>boot::boot()</code> with this function and the
original dataset supplied to perform the bootstrapping. We’ll request
199 bootstrap replications here, but in practice you should use many
@@ -1194,11 +1192,11 @@ intervals (which you can request by setting <code>type = "bca"</code>
the call to <code>boot.ci()</code>), which are known to be the most
accurate. See <code>?boot.ci</code> for details. Here, we’ll just use a
percentile confidence interval.</p>
-<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"boot"</span>)</span>
-<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">54321</span>)</span>
-<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>boot_out <span class="ot"><-</span> <span class="fu">boot</span>(d, boot_fun, <span class="at">R =</span> <span class="dv">199</span>)</span>
-<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a>boot_out</span></code></pre></div>
+<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"boot"</span>)</span>
+<span id="cb22-2"><a href="#cb22-2" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">54321</span>)</span>
+<span id="cb22-3"><a href="#cb22-3" tabindex="-1"></a>boot_out <span class="ot"><-</span> <span class="fu">boot</span>(d, boot_fun, <span class="at">R =</span> <span class="dv">199</span>)</span>
+<span id="cb22-4"><a href="#cb22-4" tabindex="-1"></a></span>
+<span id="cb22-5"><a href="#cb22-5" tabindex="-1"></a>boot_out</span></code></pre></div>
<pre><code>##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
@@ -1210,7 +1208,7 @@ percentile confidence interval.</p>
## Bootstrap Statistics :
## original bias std. error
## t1* 1.347 0.1417 0.1937</code></pre>
-<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="fu">boot.ci</span>(boot_out, <span class="at">type =</span> <span class="st">"perc"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" tabindex="-1"></a><span class="fu">boot.ci</span>(boot_out, <span class="at">type =</span> <span class="st">"perc"</span>)</span></code></pre></div>
<pre><code>## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 199 bootstrap replicates
##
@@ -1234,19 +1232,18 @@ returns the estimated quantity of interest. This function should fit the
outcome model and estimate the treatment effect using g-computation, but
the matching step occurs prior to the bootstrap. Here, we’ll use
matching without replacement, since the cluster bootstrap has been found
-to work well with it <span class="citation">(<a href="#ref-austin2014" role="doc-biblioref">Austin and Small 2014</a>; <a href="#ref-abadie2019" role="doc-biblioref">Abadie and Spiess
-2019</a>)</span>. This could be used for any method that returns pair
-membership, including other pair matching methods without replacement
-and full matching.</p>
+to work well with it <span class="citation">(<a href="#ref-austin2014">Austin and Small 2014</a>; <a href="#ref-abadie2019">Abadie and Spiess 2019</a>)</span>. This could be
+used for any method that returns pair membership, including other pair
+matching methods without replacement and full matching.</p>
<p>As before, we’ll use g-computation to estimate the marginal RR ATT,
and we’ll do so manually rather than using
<code>avg_comparisons()</code> for speed. Note that the cluster
bootstrap is already much faster than the standard bootstrap because
matching does not need to occur within each bootstrap sample. First,
we’ll do a round of matching.</p>
-<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>mNN <span class="ot"><-</span> <span class="fu">matchit</span>(A <span class="sc">~</span> X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
-<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9, <span class="at">data =</span> d)</span>
-<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a>mNN</span></code></pre></div>
+<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a>mNN <span class="ot"><-</span> <span class="fu">matchit</span>(A <span class="sc">~</span> X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
+<span id="cb26-2"><a href="#cb26-2" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9, <span class="at">data =</span> d)</span>
+<span id="cb26-3"><a href="#cb26-3" tabindex="-1"></a>mNN</span></code></pre></div>
<pre><code>## A matchit object
## - method: 1:1 nearest neighbor matching without replacement
## - distance: Propensity score
@@ -1254,46 +1251,46 @@ we’ll do a round of matching.</p>
## - number of obs.: 2000 (original), 882 (matched)
## - target estimand: ATT
## - covariates: X1, X2, X3, X4, X5, X6, X7, X8, X9</code></pre>
-<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>md <span class="ot"><-</span> <span class="fu">match.data</span>(mNN)</span></code></pre></div>
+<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" tabindex="-1"></a>md <span class="ot"><-</span> <span class="fu">match.data</span>(mNN)</span></code></pre></div>
<p>Next, we’ll write the function that takes in cluster membership and
the sampled indices and returns an estimate.</p>
-<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Unique pair IDs</span></span>
-<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>pair_ids <span class="ot"><-</span> <span class="fu">levels</span>(md<span class="sc">$</span>subclass)</span>
-<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a><span class="co">#Unit IDs, split by pair membership</span></span>
-<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a>split_inds <span class="ot"><-</span> <span class="fu">split</span>(<span class="fu">seq_len</span>(<span class="fu">nrow</span>(md)), md<span class="sc">$</span>subclass)</span>
-<span id="cb28-6"><a href="#cb28-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-7"><a href="#cb28-7" aria-hidden="true" tabindex="-1"></a>cluster_boot_fun <span class="ot"><-</span> <span class="cf">function</span>(pairs, i) {</span>
-<span id="cb28-8"><a href="#cb28-8" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb28-9"><a href="#cb28-9" aria-hidden="true" tabindex="-1"></a> <span class="co">#Extract units corresponding to selected pairs</span></span>
-<span id="cb28-10"><a href="#cb28-10" aria-hidden="true" tabindex="-1"></a> ids <span class="ot"><-</span> <span class="fu">unlist</span>(split_inds[pairs[i]])</span>
-<span id="cb28-11"><a href="#cb28-11" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb28-12"><a href="#cb28-12" aria-hidden="true" tabindex="-1"></a> <span class="co">#Subset md with block bootstrapped indices</span></span>
-<span id="cb28-13"><a href="#cb28-13" aria-hidden="true" tabindex="-1"></a> boot_md <span class="ot"><-</span> md[ids,]</span>
-<span id="cb28-14"><a href="#cb28-14" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb28-15"><a href="#cb28-15" aria-hidden="true" tabindex="-1"></a> <span class="co">#Fit outcome model</span></span>
-<span id="cb28-16"><a href="#cb28-16" aria-hidden="true" tabindex="-1"></a> fit <span class="ot"><-</span> <span class="fu">glm</span>(Y_B <span class="sc">~</span> A <span class="sc">*</span> (X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
-<span id="cb28-17"><a href="#cb28-17" aria-hidden="true" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9),</span>
-<span id="cb28-18"><a href="#cb28-18" aria-hidden="true" tabindex="-1"></a> <span class="at">data =</span> boot_md, <span class="at">weights =</span> weights,</span>
-<span id="cb28-19"><a href="#cb28-19" aria-hidden="true" tabindex="-1"></a> <span class="at">family =</span> <span class="fu">quasibinomial</span>())</span>
-<span id="cb28-20"><a href="#cb28-20" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb28-21"><a href="#cb28-21" aria-hidden="true" tabindex="-1"></a> <span class="do">## G-computation ##</span></span>
-<span id="cb28-22"><a href="#cb28-22" aria-hidden="true" tabindex="-1"></a> <span class="co">#Subset to treated units for ATT; skip for ATE</span></span>
-<span id="cb28-23"><a href="#cb28-23" aria-hidden="true" tabindex="-1"></a> md1 <span class="ot"><-</span> <span class="fu">subset</span>(boot_md, A <span class="sc">==</span> <span class="dv">1</span>)</span>
-<span id="cb28-24"><a href="#cb28-24" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb28-25"><a href="#cb28-25" aria-hidden="true" tabindex="-1"></a> <span class="co">#Estimated potential outcomes under treatment</span></span>
-<span id="cb28-26"><a href="#cb28-26" aria-hidden="true" tabindex="-1"></a> p1 <span class="ot"><-</span> <span class="fu">predict</span>(fit, <span class="at">type =</span> <span class="st">"response"</span>,</span>
-<span id="cb28-27"><a href="#cb28-27" aria-hidden="true" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">transform</span>(md1, <span class="at">A =</span> <span class="dv">1</span>))</span>
-<span id="cb28-28"><a href="#cb28-28" aria-hidden="true" tabindex="-1"></a> Ep1 <span class="ot"><-</span> <span class="fu">weighted.mean</span>(p1, md1<span class="sc">$</span>weights)</span>
-<span id="cb28-29"><a href="#cb28-29" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb28-30"><a href="#cb28-30" aria-hidden="true" tabindex="-1"></a> <span class="co">#Estimated potential outcomes under control</span></span>
-<span id="cb28-31"><a href="#cb28-31" aria-hidden="true" tabindex="-1"></a> p0 <span class="ot"><-</span> <span class="fu">predict</span>(fit, <span class="at">type =</span> <span class="st">"response"</span>,</span>
-<span id="cb28-32"><a href="#cb28-32" aria-hidden="true" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">transform</span>(md1, <span class="at">A =</span> <span class="dv">0</span>))</span>
-<span id="cb28-33"><a href="#cb28-33" aria-hidden="true" tabindex="-1"></a> Ep0 <span class="ot"><-</span> <span class="fu">weighted.mean</span>(p0, md1<span class="sc">$</span>weights)</span>
-<span id="cb28-34"><a href="#cb28-34" aria-hidden="true" tabindex="-1"></a> </span>
-<span id="cb28-35"><a href="#cb28-35" aria-hidden="true" tabindex="-1"></a> <span class="co">#Risk ratio</span></span>
-<span id="cb28-36"><a href="#cb28-36" aria-hidden="true" tabindex="-1"></a> <span class="fu">return</span>(Ep1 <span class="sc">/</span> Ep0)</span>
-<span id="cb28-37"><a href="#cb28-37" aria-hidden="true" tabindex="-1"></a>}</span></code></pre></div>
+<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" tabindex="-1"></a><span class="co">#Unique pair IDs</span></span>
+<span id="cb29-2"><a href="#cb29-2" tabindex="-1"></a>pair_ids <span class="ot"><-</span> <span class="fu">levels</span>(md<span class="sc">$</span>subclass)</span>
+<span id="cb29-3"><a href="#cb29-3" tabindex="-1"></a></span>
+<span id="cb29-4"><a href="#cb29-4" tabindex="-1"></a><span class="co">#Unit IDs, split by pair membership</span></span>
+<span id="cb29-5"><a href="#cb29-5" tabindex="-1"></a>split_inds <span class="ot"><-</span> <span class="fu">split</span>(<span class="fu">seq_len</span>(<span class="fu">nrow</span>(md)), md<span class="sc">$</span>subclass)</span>
+<span id="cb29-6"><a href="#cb29-6" tabindex="-1"></a></span>
+<span id="cb29-7"><a href="#cb29-7" tabindex="-1"></a>cluster_boot_fun <span class="ot"><-</span> <span class="cf">function</span>(pairs, i) {</span>
+<span id="cb29-8"><a href="#cb29-8" tabindex="-1"></a> </span>
+<span id="cb29-9"><a href="#cb29-9" tabindex="-1"></a> <span class="co">#Extract units corresponding to selected pairs</span></span>
+<span id="cb29-10"><a href="#cb29-10" tabindex="-1"></a> ids <span class="ot"><-</span> <span class="fu">unlist</span>(split_inds[pairs[i]])</span>
+<span id="cb29-11"><a href="#cb29-11" tabindex="-1"></a> </span>
+<span id="cb29-12"><a href="#cb29-12" tabindex="-1"></a> <span class="co">#Subset md with block bootstrapped indices</span></span>
+<span id="cb29-13"><a href="#cb29-13" tabindex="-1"></a> boot_md <span class="ot"><-</span> md[ids,]</span>
+<span id="cb29-14"><a href="#cb29-14" tabindex="-1"></a> </span>
+<span id="cb29-15"><a href="#cb29-15" tabindex="-1"></a> <span class="co">#Fit outcome model</span></span>
+<span id="cb29-16"><a href="#cb29-16" tabindex="-1"></a> fit <span class="ot"><-</span> <span class="fu">glm</span>(Y_B <span class="sc">~</span> A <span class="sc">*</span> (X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
+<span id="cb29-17"><a href="#cb29-17" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9),</span>
+<span id="cb29-18"><a href="#cb29-18" tabindex="-1"></a> <span class="at">data =</span> boot_md, <span class="at">weights =</span> weights,</span>
+<span id="cb29-19"><a href="#cb29-19" tabindex="-1"></a> <span class="at">family =</span> <span class="fu">quasibinomial</span>())</span>
+<span id="cb29-20"><a href="#cb29-20" tabindex="-1"></a> </span>
+<span id="cb29-21"><a href="#cb29-21" tabindex="-1"></a> <span class="do">## G-computation ##</span></span>
+<span id="cb29-22"><a href="#cb29-22" tabindex="-1"></a> <span class="co">#Subset to treated units for ATT; skip for ATE</span></span>
+<span id="cb29-23"><a href="#cb29-23" tabindex="-1"></a> md1 <span class="ot"><-</span> <span class="fu">subset</span>(boot_md, A <span class="sc">==</span> <span class="dv">1</span>)</span>
+<span id="cb29-24"><a href="#cb29-24" tabindex="-1"></a> </span>
+<span id="cb29-25"><a href="#cb29-25" tabindex="-1"></a> <span class="co">#Estimated potential outcomes under treatment</span></span>
+<span id="cb29-26"><a href="#cb29-26" tabindex="-1"></a> p1 <span class="ot"><-</span> <span class="fu">predict</span>(fit, <span class="at">type =</span> <span class="st">"response"</span>,</span>
+<span id="cb29-27"><a href="#cb29-27" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">transform</span>(md1, <span class="at">A =</span> <span class="dv">1</span>))</span>
+<span id="cb29-28"><a href="#cb29-28" tabindex="-1"></a> Ep1 <span class="ot"><-</span> <span class="fu">weighted.mean</span>(p1, md1<span class="sc">$</span>weights)</span>
+<span id="cb29-29"><a href="#cb29-29" tabindex="-1"></a> </span>
+<span id="cb29-30"><a href="#cb29-30" tabindex="-1"></a> <span class="co">#Estimated potential outcomes under control</span></span>
+<span id="cb29-31"><a href="#cb29-31" tabindex="-1"></a> p0 <span class="ot"><-</span> <span class="fu">predict</span>(fit, <span class="at">type =</span> <span class="st">"response"</span>,</span>
+<span id="cb29-32"><a href="#cb29-32" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">transform</span>(md1, <span class="at">A =</span> <span class="dv">0</span>))</span>
+<span id="cb29-33"><a href="#cb29-33" tabindex="-1"></a> Ep0 <span class="ot"><-</span> <span class="fu">weighted.mean</span>(p0, md1<span class="sc">$</span>weights)</span>
+<span id="cb29-34"><a href="#cb29-34" tabindex="-1"></a> </span>
+<span id="cb29-35"><a href="#cb29-35" tabindex="-1"></a> <span class="co">#Risk ratio</span></span>
+<span id="cb29-36"><a href="#cb29-36" tabindex="-1"></a> <span class="fu">return</span>(Ep1 <span class="sc">/</span> Ep0)</span>
+<span id="cb29-37"><a href="#cb29-37" tabindex="-1"></a>}</span></code></pre></div>
<p>Next, we call <code>boot::boot()</code> with this function and the
vector of pair membership supplied to perform the bootstrapping. We’ll
request 199 bootstrap replications, but in practice you should use many
@@ -1302,12 +1299,12 @@ to use the bias-corrected and accelerated (BCa) boot strap confidence
intervals, which are known to be the most accurate. See
<code>?boot.ci</code> for details. Here, we’ll just use a percentile
confidence interval.</p>
-<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"boot"</span>)</span>
-<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">54321</span>)</span>
-<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>cluster_boot_out <span class="ot"><-</span> <span class="fu">boot</span>(pair_ids, cluster_boot_fun,</span>
-<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a> <span class="at">R =</span> <span class="dv">199</span>)</span>
-<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a>cluster_boot_out</span></code></pre></div>
+<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"boot"</span>)</span>
+<span id="cb30-2"><a href="#cb30-2" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">54321</span>)</span>
+<span id="cb30-3"><a href="#cb30-3" tabindex="-1"></a>cluster_boot_out <span class="ot"><-</span> <span class="fu">boot</span>(pair_ids, cluster_boot_fun,</span>
+<span id="cb30-4"><a href="#cb30-4" tabindex="-1"></a> <span class="at">R =</span> <span class="dv">199</span>)</span>
+<span id="cb30-5"><a href="#cb30-5" tabindex="-1"></a></span>
+<span id="cb30-6"><a href="#cb30-6" tabindex="-1"></a>cluster_boot_out</span></code></pre></div>
<pre><code>##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
@@ -1319,7 +1316,7 @@ confidence interval.</p>
## Bootstrap Statistics :
## original bias std. error
## t1* 1.588 0.01304 0.1313</code></pre>
-<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="fu">boot.ci</span>(cluster_boot_out, <span class="at">type =</span> <span class="st">"perc"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" tabindex="-1"></a><span class="fu">boot.ci</span>(cluster_boot_out, <span class="at">type =</span> <span class="st">"perc"</span>)</span></code></pre></div>
<pre><code>## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 199 bootstrap replicates
##
@@ -1341,25 +1338,28 @@ in <code>cluster_boot_fun()</code> to be <code>Ep1 - Ep0</code>.</p>
<p>Moderation analysis involves determining whether a treatment effect
differs across levels of another variable. The use of matching with
moderation analysis is described in <span class="citation">Green and
-Stuart (<a href="#ref-greenExaminingModerationAnalyses2014" role="doc-biblioref">2014</a>)</span>. The goal is to achieve balance
-within each subgroup of the potential moderating variable, and there are
-several ways of doing so. Broadly, one can either perform matching in
-the full dataset, requiring exact matching on the moderator, or one can
-perform completely separate analyses in each subgroup. We’ll demonstrate
-the first approach below; see the blog post <a href="https://ngreifer.github.io/blog/subgroup-analysis-psm/">“Subgroup
+Stuart (<a href="#ref-greenExaminingModerationAnalyses2014">2014</a>)</span>. The
+goal is to achieve balance within each subgroup of the potential
+moderating variable, and there are several ways of doing so. Broadly,
+one can either perform matching in the full dataset, requiring exact
+matching on the moderator, or one can perform completely separate
+analyses in each subgroup. We’ll demonstrate the first approach below;
+see the blog post <a href="https://ngreifer.github.io/blog/subgroup-analysis-psm/">“Subgroup
Analysis After Propensity Score Matching Using R”</a> by Noah Greifer
for an example of the other approach.</p>
-<p>There are benefits to using either approach, and <span class="citation">Green and Stuart (<a href="#ref-greenExaminingModerationAnalyses2014" role="doc-biblioref">2014</a>)</span> find that either can be successful
-at balancing the subgroups. The first approach may be most effective
-with small samples, where separate propensity score models would be fit
-with greater uncertainty and an increased possibility of perfect
-prediction or failure to converge <span class="citation">(<a href="#ref-wangRelativePerformancePropensity2018" role="doc-biblioref">Wang et al. 2018</a>)</span>. The second approach
-may be more effective with larger samples or with matching methods that
-target balance in the matched sample, such as genetic matching <span class="citation">(<a href="#ref-kreifMethodsEstimatingSubgroup2012" role="doc-biblioref">Kreif et al. 2012</a>)</span>. With genetic
-matching, separate subgroup analyses ensure balance is optimized within
-each subgroup rather than just overall. The chosen approach should be
-that which achieves the best balance, though we don’t demonstrate
-assessing balance here to maintain focus on effect estimation.</p>
+<p>There are benefits to using either approach, and <span class="citation">Green and Stuart (<a href="#ref-greenExaminingModerationAnalyses2014">2014</a>)</span> find
+that either can be successful at balancing the subgroups. The first
+approach may be most effective with small samples, where separate
+propensity score models would be fit with greater uncertainty and an
+increased possibility of perfect prediction or failure to converge <span class="citation">(<a href="#ref-wangRelativePerformancePropensity2018">Wang et al.
+2018</a>)</span>. The second approach may be more effective with larger
+samples or with matching methods that target balance in the matched
+sample, such as genetic matching <span class="citation">(<a href="#ref-kreifMethodsEstimatingSubgroup2012">Kreif et al.
+2012</a>)</span>. With genetic matching, separate subgroup analyses
+ensure balance is optimized within each subgroup rather than just
+overall. The chosen approach should be that which achieves the best
+balance, though we don’t demonstrate assessing balance here to maintain
+focus on effect estimation.</p>
<p>The full dataset approach involves pooling information across
subgroups. This could involve estimating propensity scores using a
single model for both groups but exact matching on the potential
@@ -1374,10 +1374,10 @@ Below, we’ll estimate a propensity score using a single propensity score
model with a few moderator-by-covariate interactions. We’ll perform
nearest neighbor matching on the propensity score and exact matching on
the moderator, <code>X5</code>.</p>
-<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a>mP <span class="ot"><-</span> <span class="fu">matchit</span>(A <span class="sc">~</span> X1 <span class="sc">+</span> X2 <span class="sc">+</span> X5<span class="sc">*</span>X3 <span class="sc">+</span> X4 <span class="sc">+</span> </span>
-<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a> X5<span class="sc">*</span>X6 <span class="sc">+</span> X7 <span class="sc">+</span> X5<span class="sc">*</span>X8 <span class="sc">+</span> X9, <span class="at">data =</span> d,</span>
-<span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a> <span class="at">exact =</span> <span class="sc">~</span>X5, <span class="at">method =</span> <span class="st">"nearest"</span>)</span>
-<span id="cb33-4"><a href="#cb33-4" aria-hidden="true" tabindex="-1"></a>mP</span></code></pre></div>
+<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" tabindex="-1"></a>mP <span class="ot"><-</span> <span class="fu">matchit</span>(A <span class="sc">~</span> X1 <span class="sc">+</span> X2 <span class="sc">+</span> X5<span class="sc">*</span>X3 <span class="sc">+</span> X4 <span class="sc">+</span> </span>
+<span id="cb34-2"><a href="#cb34-2" tabindex="-1"></a> X5<span class="sc">*</span>X6 <span class="sc">+</span> X7 <span class="sc">+</span> X5<span class="sc">*</span>X8 <span class="sc">+</span> X9, <span class="at">data =</span> d,</span>
+<span id="cb34-3"><a href="#cb34-3" tabindex="-1"></a> <span class="at">exact =</span> <span class="sc">~</span>X5, <span class="at">method =</span> <span class="st">"nearest"</span>)</span>
+<span id="cb34-4"><a href="#cb34-4" tabindex="-1"></a>mP</span></code></pre></div>
<pre><code>## A matchit object
## - method: 1:1 nearest neighbor matching without replacement
## - distance: Propensity score
@@ -1396,42 +1396,40 @@ Segmented Data” on the <code>cobalt</code> <a href="https://ngreifer.github.io
details.</p>
<p>If we are satisfied with balance, we can then model the outcome with
an interaction between the treatment and the moderator.</p>
-<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a>mdP <span class="ot"><-</span> <span class="fu">match.data</span>(mP)</span>
-<span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb35-3"><a href="#cb35-3" aria-hidden="true" tabindex="-1"></a>fitP <span class="ot"><-</span> <span class="fu">lm</span>(Y_C <span class="sc">~</span> A <span class="sc">*</span> X5, <span class="at">data =</span> mdP, <span class="at">weights =</span> weights)</span></code></pre></div>
+<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" tabindex="-1"></a>mdP <span class="ot"><-</span> <span class="fu">match.data</span>(mP)</span>
+<span id="cb36-2"><a href="#cb36-2" tabindex="-1"></a></span>
+<span id="cb36-3"><a href="#cb36-3" tabindex="-1"></a>fitP <span class="ot"><-</span> <span class="fu">lm</span>(Y_C <span class="sc">~</span> A <span class="sc">*</span> X5, <span class="at">data =</span> mdP, <span class="at">weights =</span> weights)</span></code></pre></div>
<p>To estimate the subgroup ATTs, we can use
<code>avg_comparisons()</code>, this time specifying the <code>by</code>
argument to signify that we want treatment effects stratified by the
moderator.</p>
-<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a><span class="fu">avg_comparisons</span>(fitP, <span class="at">variables =</span> <span class="st">"A"</span>,</span>
-<span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a> <span class="at">vcov =</span> <span class="sc">~</span>subclass,</span>
-<span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">subset</span>(md, A <span class="sc">==</span> <span class="dv">1</span>),</span>
-<span id="cb36-4"><a href="#cb36-4" aria-hidden="true" tabindex="-1"></a> <span class="at">wts =</span> <span class="st">"weights"</span>,</span>
-<span id="cb36-5"><a href="#cb36-5" aria-hidden="true" tabindex="-1"></a> <span class="at">by =</span> <span class="st">"X5"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" tabindex="-1"></a><span class="fu">avg_comparisons</span>(fitP, <span class="at">variables =</span> <span class="st">"A"</span>,</span>
+<span id="cb37-2"><a href="#cb37-2" tabindex="-1"></a> <span class="at">vcov =</span> <span class="sc">~</span>subclass,</span>
+<span id="cb37-3"><a href="#cb37-3" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">subset</span>(md, A <span class="sc">==</span> <span class="dv">1</span>),</span>
+<span id="cb37-4"><a href="#cb37-4" tabindex="-1"></a> <span class="at">wts =</span> <span class="st">"weights"</span>,</span>
+<span id="cb37-5"><a href="#cb37-5" tabindex="-1"></a> <span class="at">by =</span> <span class="st">"X5"</span>)</span></code></pre></div>
<pre><code>##
## Term Contrast X5 Estimate Std. Error z Pr(>|z|) 2.5 % 97.5 %
-## A mean(1) - mean(0) 1 2.18 0.569 3.83 0.00013 1.065 3.29
-## A mean(1) - mean(0) 0 2.21 0.670 3.29 0.00099 0.894 3.52
+## A mean(1) - mean(0) 1 2.18 0.569 3.83 <0.001 1.065 3.29
+## A mean(1) - mean(0) 0 2.21 0.670 3.29 <0.001 0.894 3.52
##
-## Prediction type: response
-## Columns: type, term, contrast, X5, estimate, std.error, statistic, p.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo</code></pre>
+## Columns: term, contrast, X5, estimate, std.error, statistic, p.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo</code></pre>
<p>We can see that the subgroup mean differences are quite similar to
each other. Finally, we can test for moderation using another call to
<code>avg_comparisons()</code>, this time using the
<code>hypothesis</code> argument to signify that we want to compare
effects between subgroups:</p>
-<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a><span class="fu">avg_comparisons</span>(fitP, <span class="at">variables =</span> <span class="st">"A"</span>,</span>
-<span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a> <span class="at">vcov =</span> <span class="sc">~</span>subclass,</span>
-<span id="cb38-3"><a href="#cb38-3" aria-hidden="true" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">subset</span>(md, A <span class="sc">==</span> <span class="dv">1</span>),</span>
-<span id="cb38-4"><a href="#cb38-4" aria-hidden="true" tabindex="-1"></a> <span class="at">wts =</span> <span class="st">"weights"</span>,</span>
-<span id="cb38-5"><a href="#cb38-5" aria-hidden="true" tabindex="-1"></a> <span class="at">by =</span> <span class="st">"X5"</span>,</span>
-<span id="cb38-6"><a href="#cb38-6" aria-hidden="true" tabindex="-1"></a> <span class="at">hypothesis =</span> <span class="st">"pairwise"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb39"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb39-1"><a href="#cb39-1" tabindex="-1"></a><span class="fu">avg_comparisons</span>(fitP, <span class="at">variables =</span> <span class="st">"A"</span>,</span>
+<span id="cb39-2"><a href="#cb39-2" tabindex="-1"></a> <span class="at">vcov =</span> <span class="sc">~</span>subclass,</span>
+<span id="cb39-3"><a href="#cb39-3" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">subset</span>(md, A <span class="sc">==</span> <span class="dv">1</span>),</span>
+<span id="cb39-4"><a href="#cb39-4" tabindex="-1"></a> <span class="at">wts =</span> <span class="st">"weights"</span>,</span>
+<span id="cb39-5"><a href="#cb39-5" tabindex="-1"></a> <span class="at">by =</span> <span class="st">"X5"</span>,</span>
+<span id="cb39-6"><a href="#cb39-6" tabindex="-1"></a> <span class="at">hypothesis =</span> <span class="st">"pairwise"</span>)</span></code></pre></div>
<pre><code>##
-## Term Estimate Std. Error z Pr(>|z|) 2.5 % 97.5 %
-## (1,A,mean(1) - mean(0)) - (0,A,mean(1) - mean(0)) -0.0275 0.879 -0.0313 0.98 -1.75 1.7
+## Term Estimate Std. Error z Pr(>|z|) 2.5 % 97.5 %
+## 1 - 0 -0.0275 0.879 -0.0313 0.975 -1.75 1.7
##
-## Prediction type: response
-## Columns: type, term, estimate, std.error, statistic, p.value, conf.low, conf.high</code></pre>
+## Columns: term, estimate, std.error, statistic, p.value, conf.low, conf.high</code></pre>
<p>As expected, the difference between the subgroup treatment effects is
small and nonsignificant, so there is no evidence of moderation by
<code>X5</code>.</p>
@@ -1736,68 +1734,68 @@ Coefficients.”</span> <em>American Journal of Epidemiology</em> 177 (4):
</div>
<div id="code-to-generate-data-used-in-examples" class="section level2">
<h2>Code to Generate Data used in Examples</h2>
-<div class="sourceCode" id="cb40"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Generating data similar to Austin (2009) for demonstrating treatment effect estimation</span></span>
-<span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a>gen_X <span class="ot"><-</span> <span class="cf">function</span>(n) {</span>
-<span id="cb40-3"><a href="#cb40-3" aria-hidden="true" tabindex="-1"></a> X <span class="ot"><-</span> <span class="fu">matrix</span>(<span class="fu">rnorm</span>(<span class="dv">9</span> <span class="sc">*</span> n), <span class="at">nrow =</span> n, <span class="at">ncol =</span> <span class="dv">9</span>)</span>
-<span id="cb40-4"><a href="#cb40-4" aria-hidden="true" tabindex="-1"></a> X[,<span class="dv">5</span>] <span class="ot"><-</span> <span class="fu">as.numeric</span>(X[,<span class="dv">5</span>] <span class="sc"><</span> .<span class="dv">5</span>)</span>
-<span id="cb40-5"><a href="#cb40-5" aria-hidden="true" tabindex="-1"></a> X</span>
-<span id="cb40-6"><a href="#cb40-6" aria-hidden="true" tabindex="-1"></a>}</span>
-<span id="cb40-7"><a href="#cb40-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb40-8"><a href="#cb40-8" aria-hidden="true" tabindex="-1"></a><span class="co">#~20% treated</span></span>
-<span id="cb40-9"><a href="#cb40-9" aria-hidden="true" tabindex="-1"></a>gen_A <span class="ot"><-</span> <span class="cf">function</span>(X) {</span>
-<span id="cb40-10"><a href="#cb40-10" aria-hidden="true" tabindex="-1"></a> LP_A <span class="ot"><-</span> <span class="sc">-</span> <span class="fl">1.2</span> <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">1</span>] <span class="sc">-</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">2</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">4</span>] <span class="sc">-</span> <span class="fu">log</span>(<span class="fl">2.4</span>)<span class="sc">*</span>X[,<span class="dv">5</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">7</span>] <span class="sc">-</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">8</span>]</span>
-<span id="cb40-11"><a href="#cb40-11" aria-hidden="true" tabindex="-1"></a> P_A <span class="ot"><-</span> <span class="fu">plogis</span>(LP_A)</span>
-<span id="cb40-12"><a href="#cb40-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">rbinom</span>(<span class="fu">nrow</span>(X), <span class="dv">1</span>, P_A)</span>
-<span id="cb40-13"><a href="#cb40-13" aria-hidden="true" tabindex="-1"></a>}</span>
-<span id="cb40-14"><a href="#cb40-14" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb40-15"><a href="#cb40-15" aria-hidden="true" tabindex="-1"></a><span class="co"># Continuous outcome</span></span>
-<span id="cb40-16"><a href="#cb40-16" aria-hidden="true" tabindex="-1"></a>gen_Y_C <span class="ot"><-</span> <span class="cf">function</span>(A, X) {</span>
-<span id="cb40-17"><a href="#cb40-17" aria-hidden="true" tabindex="-1"></a> <span class="dv">2</span><span class="sc">*</span>A <span class="sc">+</span> <span class="dv">2</span><span class="sc">*</span>X[,<span class="dv">1</span>] <span class="sc">+</span> <span class="dv">2</span><span class="sc">*</span>X[,<span class="dv">2</span>] <span class="sc">+</span> <span class="dv">2</span><span class="sc">*</span>X[,<span class="dv">3</span>] <span class="sc">+</span> <span class="dv">1</span><span class="sc">*</span>X[,<span class="dv">4</span>] <span class="sc">+</span> <span class="dv">2</span><span class="sc">*</span>X[,<span class="dv">5</span>] <span class="sc">+</span> <span class="dv">1</span><span class="sc">*</span>X[,<span class="dv">6</span>] <span class="sc">+</span> <span class="fu">rnorm</span>(<span class="fu">length</span>(A), <span class="dv">0</span>, <span class="dv">5</span>)</span>
-<span id="cb40-18"><a href="#cb40-18" aria-hidden="true" tabindex="-1"></a>}</span>
-<span id="cb40-19"><a href="#cb40-19" aria-hidden="true" tabindex="-1"></a><span class="co">#Conditional:</span></span>
-<span id="cb40-20"><a href="#cb40-20" aria-hidden="true" tabindex="-1"></a><span class="co"># MD: 2</span></span>
-<span id="cb40-21"><a href="#cb40-21" aria-hidden="true" tabindex="-1"></a><span class="co">#Marginal:</span></span>
-<span id="cb40-22"><a href="#cb40-22" aria-hidden="true" tabindex="-1"></a><span class="co"># MD: 2</span></span>
-<span id="cb40-23"><a href="#cb40-23" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb40-24"><a href="#cb40-24" aria-hidden="true" tabindex="-1"></a><span class="co"># Binary outcome</span></span>
-<span id="cb40-25"><a href="#cb40-25" aria-hidden="true" tabindex="-1"></a>gen_Y_B <span class="ot"><-</span> <span class="cf">function</span>(A, X) {</span>
-<span id="cb40-26"><a href="#cb40-26" aria-hidden="true" tabindex="-1"></a> LP_B <span class="ot"><-</span> <span class="sc">-</span><span class="dv">2</span> <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">2.4</span>)<span class="sc">*</span>A <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">1</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">2</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">3</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">4</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">2.4</span>)<span class="sc">*</span>X[,<span class="dv">5</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">6</span>]</span>
-<span id="cb40-27"><a href="#cb40-27" aria-hidden="true" tabindex="-1"></a> P_B <span class="ot"><-</span> <span class="fu">plogis</span>(LP_B)</span>
-<span id="cb40-28"><a href="#cb40-28" aria-hidden="true" tabindex="-1"></a> <span class="fu">rbinom</span>(<span class="fu">length</span>(A), <span class="dv">1</span>, P_B)</span>
-<span id="cb40-29"><a href="#cb40-29" aria-hidden="true" tabindex="-1"></a>}</span>
-<span id="cb40-30"><a href="#cb40-30" aria-hidden="true" tabindex="-1"></a><span class="co">#Conditional:</span></span>
-<span id="cb40-31"><a href="#cb40-31" aria-hidden="true" tabindex="-1"></a><span class="co"># OR: 2.4</span></span>
-<span id="cb40-32"><a href="#cb40-32" aria-hidden="true" tabindex="-1"></a><span class="co"># logOR: .875</span></span>
-<span id="cb40-33"><a href="#cb40-33" aria-hidden="true" tabindex="-1"></a><span class="co">#Marginal:</span></span>
-<span id="cb40-34"><a href="#cb40-34" aria-hidden="true" tabindex="-1"></a><span class="co"># RD: .144</span></span>
-<span id="cb40-35"><a href="#cb40-35" aria-hidden="true" tabindex="-1"></a><span class="co"># RR: 1.54</span></span>
-<span id="cb40-36"><a href="#cb40-36" aria-hidden="true" tabindex="-1"></a><span class="co"># logRR: .433</span></span>
-<span id="cb40-37"><a href="#cb40-37" aria-hidden="true" tabindex="-1"></a><span class="co"># OR: 1.92</span></span>
-<span id="cb40-38"><a href="#cb40-38" aria-hidden="true" tabindex="-1"></a><span class="co"># logOR .655</span></span>
-<span id="cb40-39"><a href="#cb40-39" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb40-40"><a href="#cb40-40" aria-hidden="true" tabindex="-1"></a><span class="co"># Survival outcome</span></span>
-<span id="cb40-41"><a href="#cb40-41" aria-hidden="true" tabindex="-1"></a>gen_Y_S <span class="ot"><-</span> <span class="cf">function</span>(A, X) {</span>
-<span id="cb40-42"><a href="#cb40-42" aria-hidden="true" tabindex="-1"></a> LP_S <span class="ot"><-</span> <span class="sc">-</span><span class="dv">2</span> <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">2.4</span>)<span class="sc">*</span>A <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">1</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">2</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">3</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">4</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">2.4</span>)<span class="sc">*</span>X[,<span class="dv">5</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">6</span>]</span>
-<span id="cb40-43"><a href="#cb40-43" aria-hidden="true" tabindex="-1"></a> <span class="fu">sqrt</span>(<span class="sc">-</span><span class="fu">log</span>(<span class="fu">runif</span>(<span class="fu">length</span>(A)))<span class="sc">*</span><span class="fl">2e4</span><span class="sc">*</span><span class="fu">exp</span>(<span class="sc">-</span>LP_S))</span>
-<span id="cb40-44"><a href="#cb40-44" aria-hidden="true" tabindex="-1"></a>}</span>
-<span id="cb40-45"><a href="#cb40-45" aria-hidden="true" tabindex="-1"></a><span class="co">#Conditional:</span></span>
-<span id="cb40-46"><a href="#cb40-46" aria-hidden="true" tabindex="-1"></a><span class="co"># HR: 2.4</span></span>
-<span id="cb40-47"><a href="#cb40-47" aria-hidden="true" tabindex="-1"></a><span class="co"># logHR: .875</span></span>
-<span id="cb40-48"><a href="#cb40-48" aria-hidden="true" tabindex="-1"></a><span class="co">#Marginal:</span></span>
-<span id="cb40-49"><a href="#cb40-49" aria-hidden="true" tabindex="-1"></a><span class="co"># HR: 1.57</span></span>
-<span id="cb40-50"><a href="#cb40-50" aria-hidden="true" tabindex="-1"></a><span class="co"># logHR: .452</span></span>
-<span id="cb40-51"><a href="#cb40-51" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb40-52"><a href="#cb40-52" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">19599</span>)</span>
-<span id="cb40-53"><a href="#cb40-53" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb40-54"><a href="#cb40-54" aria-hidden="true" tabindex="-1"></a>n <span class="ot"><-</span> <span class="dv">2000</span></span>
-<span id="cb40-55"><a href="#cb40-55" aria-hidden="true" tabindex="-1"></a>X <span class="ot"><-</span> <span class="fu">gen_X</span>(n)</span>
-<span id="cb40-56"><a href="#cb40-56" aria-hidden="true" tabindex="-1"></a>A <span class="ot"><-</span> <span class="fu">gen_A</span>(X)</span>
-<span id="cb40-57"><a href="#cb40-57" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb40-58"><a href="#cb40-58" aria-hidden="true" tabindex="-1"></a>Y_C <span class="ot"><-</span> <span class="fu">gen_Y_C</span>(A, X)</span>
-<span id="cb40-59"><a href="#cb40-59" aria-hidden="true" tabindex="-1"></a>Y_B <span class="ot"><-</span> <span class="fu">gen_Y_B</span>(A, X)</span>
-<span id="cb40-60"><a href="#cb40-60" aria-hidden="true" tabindex="-1"></a>Y_S <span class="ot"><-</span> <span class="fu">gen_Y_S</span>(A, X)</span>
-<span id="cb40-61"><a href="#cb40-61" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb40-62"><a href="#cb40-62" aria-hidden="true" tabindex="-1"></a>d <span class="ot"><-</span> <span class="fu">data.frame</span>(A, X, Y_C, Y_B, Y_S)</span></code></pre></div>
+<div class="sourceCode" id="cb41"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" tabindex="-1"></a><span class="co">#Generating data similar to Austin (2009) for demonstrating treatment effect estimation</span></span>
+<span id="cb41-2"><a href="#cb41-2" tabindex="-1"></a>gen_X <span class="ot"><-</span> <span class="cf">function</span>(n) {</span>
+<span id="cb41-3"><a href="#cb41-3" tabindex="-1"></a> X <span class="ot"><-</span> <span class="fu">matrix</span>(<span class="fu">rnorm</span>(<span class="dv">9</span> <span class="sc">*</span> n), <span class="at">nrow =</span> n, <span class="at">ncol =</span> <span class="dv">9</span>)</span>
+<span id="cb41-4"><a href="#cb41-4" tabindex="-1"></a> X[,<span class="dv">5</span>] <span class="ot"><-</span> <span class="fu">as.numeric</span>(X[,<span class="dv">5</span>] <span class="sc"><</span> .<span class="dv">5</span>)</span>
+<span id="cb41-5"><a href="#cb41-5" tabindex="-1"></a> X</span>
+<span id="cb41-6"><a href="#cb41-6" tabindex="-1"></a>}</span>
+<span id="cb41-7"><a href="#cb41-7" tabindex="-1"></a></span>
+<span id="cb41-8"><a href="#cb41-8" tabindex="-1"></a><span class="co">#~20% treated</span></span>
+<span id="cb41-9"><a href="#cb41-9" tabindex="-1"></a>gen_A <span class="ot"><-</span> <span class="cf">function</span>(X) {</span>
+<span id="cb41-10"><a href="#cb41-10" tabindex="-1"></a> LP_A <span class="ot"><-</span> <span class="sc">-</span> <span class="fl">1.2</span> <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">1</span>] <span class="sc">-</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">2</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">4</span>] <span class="sc">-</span> <span class="fu">log</span>(<span class="fl">2.4</span>)<span class="sc">*</span>X[,<span class="dv">5</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">7</span>] <span class="sc">-</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">8</span>]</span>
+<span id="cb41-11"><a href="#cb41-11" tabindex="-1"></a> P_A <span class="ot"><-</span> <span class="fu">plogis</span>(LP_A)</span>
+<span id="cb41-12"><a href="#cb41-12" tabindex="-1"></a> <span class="fu">rbinom</span>(<span class="fu">nrow</span>(X), <span class="dv">1</span>, P_A)</span>
+<span id="cb41-13"><a href="#cb41-13" tabindex="-1"></a>}</span>
+<span id="cb41-14"><a href="#cb41-14" tabindex="-1"></a></span>
+<span id="cb41-15"><a href="#cb41-15" tabindex="-1"></a><span class="co"># Continuous outcome</span></span>
+<span id="cb41-16"><a href="#cb41-16" tabindex="-1"></a>gen_Y_C <span class="ot"><-</span> <span class="cf">function</span>(A, X) {</span>
+<span id="cb41-17"><a href="#cb41-17" tabindex="-1"></a> <span class="dv">2</span><span class="sc">*</span>A <span class="sc">+</span> <span class="dv">2</span><span class="sc">*</span>X[,<span class="dv">1</span>] <span class="sc">+</span> <span class="dv">2</span><span class="sc">*</span>X[,<span class="dv">2</span>] <span class="sc">+</span> <span class="dv">2</span><span class="sc">*</span>X[,<span class="dv">3</span>] <span class="sc">+</span> <span class="dv">1</span><span class="sc">*</span>X[,<span class="dv">4</span>] <span class="sc">+</span> <span class="dv">2</span><span class="sc">*</span>X[,<span class="dv">5</span>] <span class="sc">+</span> <span class="dv">1</span><span class="sc">*</span>X[,<span class="dv">6</span>] <span class="sc">+</span> <span class="fu">rnorm</span>(<span class="fu">length</span>(A), <span class="dv">0</span>, <span class="dv">5</span>)</span>
+<span id="cb41-18"><a href="#cb41-18" tabindex="-1"></a>}</span>
+<span id="cb41-19"><a href="#cb41-19" tabindex="-1"></a><span class="co">#Conditional:</span></span>
+<span id="cb41-20"><a href="#cb41-20" tabindex="-1"></a><span class="co"># MD: 2</span></span>
+<span id="cb41-21"><a href="#cb41-21" tabindex="-1"></a><span class="co">#Marginal:</span></span>
+<span id="cb41-22"><a href="#cb41-22" tabindex="-1"></a><span class="co"># MD: 2</span></span>
+<span id="cb41-23"><a href="#cb41-23" tabindex="-1"></a></span>
+<span id="cb41-24"><a href="#cb41-24" tabindex="-1"></a><span class="co"># Binary outcome</span></span>
+<span id="cb41-25"><a href="#cb41-25" tabindex="-1"></a>gen_Y_B <span class="ot"><-</span> <span class="cf">function</span>(A, X) {</span>
+<span id="cb41-26"><a href="#cb41-26" tabindex="-1"></a> LP_B <span class="ot"><-</span> <span class="sc">-</span><span class="dv">2</span> <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">2.4</span>)<span class="sc">*</span>A <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">1</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">2</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">3</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">4</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">2.4</span>)<span class="sc">*</span>X[,<span class="dv">5</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">6</span>]</span>
+<span id="cb41-27"><a href="#cb41-27" tabindex="-1"></a> P_B <span class="ot"><-</span> <span class="fu">plogis</span>(LP_B)</span>
+<span id="cb41-28"><a href="#cb41-28" tabindex="-1"></a> <span class="fu">rbinom</span>(<span class="fu">length</span>(A), <span class="dv">1</span>, P_B)</span>
+<span id="cb41-29"><a href="#cb41-29" tabindex="-1"></a>}</span>
+<span id="cb41-30"><a href="#cb41-30" tabindex="-1"></a><span class="co">#Conditional:</span></span>
+<span id="cb41-31"><a href="#cb41-31" tabindex="-1"></a><span class="co"># OR: 2.4</span></span>
+<span id="cb41-32"><a href="#cb41-32" tabindex="-1"></a><span class="co"># logOR: .875</span></span>
+<span id="cb41-33"><a href="#cb41-33" tabindex="-1"></a><span class="co">#Marginal:</span></span>
+<span id="cb41-34"><a href="#cb41-34" tabindex="-1"></a><span class="co"># RD: .144</span></span>
+<span id="cb41-35"><a href="#cb41-35" tabindex="-1"></a><span class="co"># RR: 1.54</span></span>
+<span id="cb41-36"><a href="#cb41-36" tabindex="-1"></a><span class="co"># logRR: .433</span></span>
+<span id="cb41-37"><a href="#cb41-37" tabindex="-1"></a><span class="co"># OR: 1.92</span></span>
+<span id="cb41-38"><a href="#cb41-38" tabindex="-1"></a><span class="co"># logOR .655</span></span>
+<span id="cb41-39"><a href="#cb41-39" tabindex="-1"></a></span>
+<span id="cb41-40"><a href="#cb41-40" tabindex="-1"></a><span class="co"># Survival outcome</span></span>
+<span id="cb41-41"><a href="#cb41-41" tabindex="-1"></a>gen_Y_S <span class="ot"><-</span> <span class="cf">function</span>(A, X) {</span>
+<span id="cb41-42"><a href="#cb41-42" tabindex="-1"></a> LP_S <span class="ot"><-</span> <span class="sc">-</span><span class="dv">2</span> <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">2.4</span>)<span class="sc">*</span>A <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">1</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">2</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">3</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">4</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">2.4</span>)<span class="sc">*</span>X[,<span class="dv">5</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">6</span>]</span>
+<span id="cb41-43"><a href="#cb41-43" tabindex="-1"></a> <span class="fu">sqrt</span>(<span class="sc">-</span><span class="fu">log</span>(<span class="fu">runif</span>(<span class="fu">length</span>(A)))<span class="sc">*</span><span class="fl">2e4</span><span class="sc">*</span><span class="fu">exp</span>(<span class="sc">-</span>LP_S))</span>
+<span id="cb41-44"><a href="#cb41-44" tabindex="-1"></a>}</span>
+<span id="cb41-45"><a href="#cb41-45" tabindex="-1"></a><span class="co">#Conditional:</span></span>
+<span id="cb41-46"><a href="#cb41-46" tabindex="-1"></a><span class="co"># HR: 2.4</span></span>
+<span id="cb41-47"><a href="#cb41-47" tabindex="-1"></a><span class="co"># logHR: .875</span></span>
+<span id="cb41-48"><a href="#cb41-48" tabindex="-1"></a><span class="co">#Marginal:</span></span>
+<span id="cb41-49"><a href="#cb41-49" tabindex="-1"></a><span class="co"># HR: 1.57</span></span>
+<span id="cb41-50"><a href="#cb41-50" tabindex="-1"></a><span class="co"># logHR: .452</span></span>
+<span id="cb41-51"><a href="#cb41-51" tabindex="-1"></a></span>
+<span id="cb41-52"><a href="#cb41-52" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">19599</span>)</span>
+<span id="cb41-53"><a href="#cb41-53" tabindex="-1"></a></span>
+<span id="cb41-54"><a href="#cb41-54" tabindex="-1"></a>n <span class="ot"><-</span> <span class="dv">2000</span></span>
+<span id="cb41-55"><a href="#cb41-55" tabindex="-1"></a>X <span class="ot"><-</span> <span class="fu">gen_X</span>(n)</span>
+<span id="cb41-56"><a href="#cb41-56" tabindex="-1"></a>A <span class="ot"><-</span> <span class="fu">gen_A</span>(X)</span>
+<span id="cb41-57"><a href="#cb41-57" tabindex="-1"></a></span>
+<span id="cb41-58"><a href="#cb41-58" tabindex="-1"></a>Y_C <span class="ot"><-</span> <span class="fu">gen_Y_C</span>(A, X)</span>
+<span id="cb41-59"><a href="#cb41-59" tabindex="-1"></a>Y_B <span class="ot"><-</span> <span class="fu">gen_Y_B</span>(A, X)</span>
+<span id="cb41-60"><a href="#cb41-60" tabindex="-1"></a>Y_S <span class="ot"><-</span> <span class="fu">gen_Y_S</span>(A, X)</span>
+<span id="cb41-61"><a href="#cb41-61" tabindex="-1"></a></span>
+<span id="cb41-62"><a href="#cb41-62" tabindex="-1"></a>d <span class="ot"><-</span> <span class="fu">data.frame</span>(A, X, Y_C, Y_B, Y_S)</span></code></pre></div>
</div>
<div class="footnotes footnotes-end-of-document">
<hr />
@@ -1817,13 +1815,13 @@ the block bootstrap.<a href="#fnref2" class="footnote-back">↩︎</a></p></li>
not necessary, including them does not affect the estimates. Because it
may not always be clear when weights are required, we recommend always
including them.<a href="#fnref3" class="footnote-back">↩︎</a></p></li>
-<li id="fn4"><p>To verify that they are equal, add the argument
-<code>hypothesis = "revpairwise"</code> to the call to
-<code>predictions()</code>; this explicitly compares the average
-potential outcomes and should yield identical estimates to the
-<code>comparisons()</code> call.<a href="#fnref4" class="footnote-back">↩︎</a></p></li>
+<li id="fn4"><p>To verify that they are equal, supply the output of
+<code>avg_predictions()</code> to
+<code>hypotheses(), e.g.,</code>avg_predictions(…) |>
+hypotheses(“revpairwise”)<code>; this explicitly compares the average potential outcomes and should yield identical estimates to the</code>avg_comparisons()`
+call.<a href="#fnref4" class="footnote-back">↩︎</a></p></li>
<li id="fn5"><p>It is also known as fine stratification weighting,
-described by <span class="citation">Desai et al. (<a href="#ref-desai2017" role="doc-biblioref">2017</a>)</span>.<a href="#fnref5" class="footnote-back">↩︎</a></p></li>
+described by <span class="citation">Desai et al. (<a href="#ref-desai2017">2017</a>)</span>.<a href="#fnref5" class="footnote-back">↩︎</a></p></li>
<li id="fn6"><p>We use <code>quasibinomial()</code> instead of
<code>binomial()</code> simply to avoid a spurious warning that can
occur with certain kinds of matching; the results will be identical
@@ -1834,11 +1832,11 @@ go below 0 or above 1; this is because an approximation is used. To
avoid this problem, bootstrapping or simulation-based inference can be
used instead.<a href="#fnref7" class="footnote-back">↩︎</a></p></li>
<li id="fn8"><p>It is not immediately clear how to estimate a marginal
-HR when covariates are included in the outcome model; though <span class="citation">Austin, Thomas, and Rubin (<a href="#ref-austin2020" role="doc-biblioref">2020</a>)</span> describe several ways of including
-covariates in a model to estimate the marginal HR, they do not develop
-SEs and little research has been done on this method, so we will not
-present it here. Instead, we fit a simple Cox model with the treatment
-as the sole predictor.<a href="#fnref8" class="footnote-back">↩︎</a></p></li>
+HR when covariates are included in the outcome model; though <span class="citation">Austin, Thomas, and Rubin (<a href="#ref-austin2020">2020</a>)</span> describe several ways of
+including covariates in a model to estimate the marginal HR, they do not
+develop SEs and little research has been done on this method, so we will
+not present it here. Instead, we fit a simple Cox model with the
+treatment as the sole predictor.<a href="#fnref8" class="footnote-back">↩︎</a></p></li>
<li id="fn9"><p>For subclassification, only MMWS can be used; this is
done simply by including the stratification weights in the Cox model and
omitting the <code>cluster</code> argument.<a href="#fnref9" class="footnote-back">↩︎</a></p></li>
diff --git a/inst/doc/matching-methods.Rmd b/inst/doc/matching-methods.Rmd
index 5ec18ba..ac6483a 100644
--- a/inst/doc/matching-methods.Rmd
+++ b/inst/doc/matching-methods.Rmd
@@ -155,7 +155,7 @@ To perform Mahalanobis distance matching without the need to estimate or use a p
### Exact matching (`exact`)
-To perform exact matching on all supplied covariates, the `method` argument can be set to `"exact"`. To perform exact matching only on some covariates and some other form of matching within exact matching strata on other covariates, the `exact` argument can be used. Covariates supplied to the `exact` argument will be matched exactly, and the form of matching specified by `method` (e.g., `"nearest"` for nearest neighbor matching) will take place within each exact matching stratum. This can be a good way to gain some of the benefits of exact matching without completely succumbing to the curse of dimensionality. As with exact matching performed with `method = "exact"`, any units in strata lacking members of one of the treatment groups will be left unmatched. Note that although matching occurs within each exact matching stratum, propensity score estimation and computation of the Mahalanobis distance occur in the full sample. **If units from the treated group are unmatched due to an exact matching restriction, the estimand no longer corresponds to the ATT.**
+To perform exact matching on all supplied covariates, the `method` argument can be set to `"exact"`. To perform exact matching only on some covariates and some other form of matching within exact matching strata on other covariates, the `exact` argument can be used. Covariates supplied to the `exact` argument will be matched exactly, and the form of matching specified by `method` (e.g., `"nearest"` for nearest neighbor matching) will take place within each exact matching stratum. This can be a good way to gain some of the benefits of exact matching without completely succumbing to the curse of dimensionality. As with exact matching performed with `method = "exact"`, any units in strata lacking members of one of the treatment groups will be left unmatched. Note that although matching occurs within each exact matching stratum, propensity score estimation and computation of the Mahalanobis or other distance matrix occur in the full sample. **If units from the treated group are unmatched due to an exact matching restriction, the estimand no longer corresponds to the ATT.**
### Anti-exact matching (`antiexact`)
@@ -171,7 +171,11 @@ The `reuse.max` argument can also be used with `method = "nearest"` to control h
The most common form of matching, 1:1 matching, involves pairing one control unit with each treated unit. To perform $k$:1 matching (e.g., 2:1 or 3:1), which pairs (up to) $k$ control units with each treated unit, the `ratio` argument can be specified. Performing $k$:1 matching can preserve precision by preventing too many control units from being unmatched and dropped from the matched sample, though the gain in precision by increasing $k$ diminishes rapidly after 4 [@rosenbaum2020]. Importantly, for $k>1$, the matches after the first match will generally be worse than the first match in terms of closeness to the treated unit, so increasing $k$ can also worsen balance. @austin2010a found that 1:1 or 1:2 matching generally performed best in terms of mean squared error. In general, it makes sense to use higher values of $k$ while ensuring that balance is satisfactory.
-With nearest neighbor and optimal pair matching, variable $k$:1 matching, in which the number of controls matched to each treated unit varies, can also be used; this can have improved performance over "fixed" $k$:1 matching [@ming2000]. See `?method_nearest` and `?method_optimal` for information on implementing variable $k$:1 matching.
+With nearest neighbor and optimal pair matching, variable $k$:1 matching, in which the number of controls matched to each treated unit varies, can also be used; this can have improved performance over "fixed" $k$:1 matching [@ming2000; @rassenOnetomanyPropensityScore2012]. See `?method_nearest` and `?method_optimal` for information on implementing variable $k$:1 matching.
+
+### Matching order (`m.order`)
+
+For nearest neighbor matching (including genetic matching), units are matched in an order, and that order can affect the quality of individual matches and of the resulting matched sample. With `method = "nearest"`, the allowable options to `m.order` to control the matching order are `"largest"`, `"smallest"`, `"closest"`, `"random"`, and `"data"`. With `method = "genetic"`, all but `"closest"` can be used. Requesting `"largest"` means that treated units with the largest propensity scores, i.e., those least like the control units, will be matched first, which prevents them from having bad matches after all the close control units have been used up. `"smallest"` means that treated units with the smallest propensity scores are matched first. `"closest"` means that potential pairs with the smallest distance between units will be matched first, which ensures that the best possible matches are included in the matched sample but can yield poor matches for units whose best match is far from them; this makes it particularly useful when matching with a caliper. `"random"` matches in a random order and `"data"` matches in order of the data. A propensity score is required for `"largest"` and `"smallest"` but not for the other options. @rubin1973 recommends using `"largest"` or `"random"`, though @austin2013b recommends against `"largest"` and instead favors `"closest"` or `"random"`.
## Choosing a Matching Method
diff --git a/inst/doc/matching-methods.html b/inst/doc/matching-methods.html
index 1e31f32..4478829 100644
--- a/inst/doc/matching-methods.html
+++ b/inst/doc/matching-methods.html
@@ -12,7 +12,7 @@
<meta name="author" content="Noah Greifer" />
-<meta name="date" content="2023-02-22" />
+<meta name="date" content="2023-06-13" />
<title>Matching Methods</title>
@@ -261,7 +261,7 @@ code > span.er { color: #a61717; background-color: #e3d2d2; }
<h1 class="title toc-ignore">Matching Methods</h1>
<h4 class="author">Noah Greifer</h4>
-<h4 class="date">2023-02-22</h4>
+<h4 class="date">2023-06-13</h4>
<div id="TOC">
@@ -310,6 +310,8 @@ matching (<code>exact</code>)</a></li>
(<code>replace</code>)</a></li>
<li><a href="#k1-matching-ratio" id="toc-k1-matching-ratio"><span class="math inline">\(k\)</span>:1 matching
(<code>ratio</code>)</a></li>
+<li><a href="#matching-order-m.order" id="toc-matching-order-m.order">Matching order
+(<code>m.order</code>)</a></li>
</ul></li>
<li><a href="#choosing-a-matching-method" id="toc-choosing-a-matching-method">Choosing a Matching Method</a></li>
<li><a href="#reporting-the-matching-specification" id="toc-reporting-the-matching-specification">Reporting the Matching
@@ -332,7 +334,8 @@ nonparametric preprocessing through matching is that a number of
matching methods can be tried and their quality assessed without
consulting the outcome, reducing the possibility of capitalizing on
chance while allowing for the benefits of an exploratory analysis in the
-design phase <span class="citation">(<a href="#ref-ho2007" role="doc-biblioref">Ho et al. 2007</a>)</span>.</p>
+design phase <span class="citation">(<a href="#ref-ho2007">Ho et al.
+2007</a>)</span>.</p>
<p>This vignette describes each matching method available in
<code>MatchIt</code> and the various options that are allowed with
matching methods and the consequences of their use. For a brief
@@ -354,10 +357,10 @@ comparison of the outcomes treatment and control groups is not
confounded by the measured and balanced covariates. Although statistical
estimation methods like regression can also be used to remove
confounding due to measured covariates, <span class="citation">Ho et al.
-(<a href="#ref-ho2007" role="doc-biblioref">2007</a>)</span> argue that
-fitting regression models in matched samples reduces the dependence of
-the validity of the estimated treatment effect on the correct
-specification of the model.</p>
+(<a href="#ref-ho2007">2007</a>)</span> argue that fitting regression
+models in matched samples reduces the dependence of the validity of the
+estimated treatment effect on the correct specification of the
+model.</p>
<p>Matching is nonparametric in the sense that the estimated weights and
pruning of the sample are not direct functions of estimated model
parameters but rather depend on the organization of discrete units in
@@ -369,7 +372,7 @@ as the intuitive understanding of matching by the public compared to
regression or weighting, make it a robust and effective way to estimate
treatment effects.</p>
<p>It is important to note that this implementation of matching differs
-from the methods described by Abadie and Imbens <span class="citation">(<a href="#ref-abadie2006" role="doc-biblioref">2006</a>, <a href="#ref-abadie2016" role="doc-biblioref">2016</a>)</span> and implemented in the
+from the methods described by Abadie and Imbens <span class="citation">(<a href="#ref-abadie2006">2006</a>, <a href="#ref-abadie2016">2016</a>)</span> and implemented in the
<code>Matching</code> R package and <code>teffects</code> routine in
Stata. That form of matching is <em>matching imputation</em>, where the
missing potential outcomes for each unit are imputed using the observed
@@ -393,24 +396,22 @@ experiments, which are more familiar to non-technical audiences.</p>
<p>In addition to subset selection, matching often (though not always)
involves a form of <em>stratification</em>, the assignment of units to
pairs or strata containing multiple units. The distinction between
-subset selection and stratification is described by <span class="citation">Zubizarreta, Paredes, and Rosenbaum (<a href="#ref-zubizarreta2014" role="doc-biblioref">2014a</a>)</span>, who
-separate them into two separate steps. In <code>MatchIt</code>, with
-almost all matching methods, subset selection is performed by
-stratification; for example, treated units are paired with control
-units, and unpaired units are then dropped from the matched sample. With
-some methods, subclasses are used to assign matching or stratification
-weights to individual units, which increase or decrease each unit’s
-leverage in a subsequent analysis. There has been some debate about the
-importance of stratification after subset selection; while some authors
-have argued that, with some forms of matching, pair membership is
-incidental <span class="citation">(<a href="#ref-stuart2008" role="doc-biblioref">Stuart 2008</a>; <a href="#ref-schafer2008" role="doc-biblioref">Schafer and Kang 2008</a>)</span>, others have
+subset selection and stratification is described by <span class="citation">Zubizarreta, Paredes, and Rosenbaum (<a href="#ref-zubizarreta2014">2014a</a>)</span>, who separate them into
+two separate steps. In <code>MatchIt</code>, with almost all matching
+methods, subset selection is performed by stratification; for example,
+treated units are paired with control units, and unpaired units are then
+dropped from the matched sample. With some methods, subclasses are used
+to assign matching or stratification weights to individual units, which
+increase or decrease each unit’s leverage in a subsequent analysis.
+There has been some debate about the importance of stratification after
+subset selection; while some authors have argued that, with some forms
+of matching, pair membership is incidental <span class="citation">(<a href="#ref-stuart2008">Stuart 2008</a>; <a href="#ref-schafer2008">Schafer and Kang 2008</a>)</span>, others have
argued that correctly incorporating pair membership into effect
-estimation can improve the quality of inferences <span class="citation">(<a href="#ref-austin2014a" role="doc-biblioref">Austin
-and Small 2014</a>; <a href="#ref-wan2019" role="doc-biblioref">Wan
-2019</a>)</span>. For methods that allow it, <code>MatchIt</code>
-includes stratum membership as an additional output of each matching
-specification. How these strata can be used is detailed in
-<code>vignette("Estimating Effects")</code>.</p>
+estimation can improve the quality of inferences <span class="citation">(<a href="#ref-austin2014a">Austin and Small 2014</a>;
+<a href="#ref-wan2019">Wan 2019</a>)</span>. For methods that allow it,
+<code>MatchIt</code> includes stratum membership as an additional output
+of each matching specification. How these strata can be used is detailed
+in <code>vignette("Estimating Effects")</code>.</p>
<p>At the heart of <code>MatchIt</code> are three classes of methods:
distance matching, stratum matching, and pure subset selection.
<em>Distance matching</em> involves considering a focal group (usually
@@ -471,31 +472,29 @@ closest eligible control unit to be paired with each treated unit. It is
greedy in the sense that each pairing occurs without reference to how
other units will be or have been paired, and therefore does not aim to
optimize any criterion. Nearest neighbor matching is the most common
-form of matching used <span class="citation">(<a href="#ref-thoemmes2011" role="doc-biblioref">Thoemmes and Kim 2011</a>;
-<a href="#ref-zakrison2018" role="doc-biblioref">Zakrison, Austin, and
-McCredie 2018</a>)</span> and has been extensively studied through
-simulations. See <code>?method_nearest</code> for the documentation for
+form of matching used <span class="citation">(<a href="#ref-thoemmes2011">Thoemmes and Kim 2011</a>; <a href="#ref-zakrison2018">Zakrison, Austin, and McCredie 2018</a>)</span>
+and has been extensively studied through simulations. See
+<code>?method_nearest</code> for the documentation for
<code>matchit()</code> with <code>method = "nearest"</code>.</p>
<p>Nearest neighbor matching requires the specification of a distance
measure to define which control unit is closest to each treated unit.
The default and most common distance is the <em>propensity score
difference</em>, which is the difference between the propensity scores
-of each treated and control unit <span class="citation">(<a href="#ref-stuart2010" role="doc-biblioref">Stuart 2010</a>)</span>.
-Another popular distance is the Mahalanobis distance, described in the
-section “Mahalanobis distance matching” below. The order in which the
-treated units are to be paired must also be specified and has the
-potential to change the quality of the matches <span class="citation">(<a href="#ref-austin2013b" role="doc-biblioref">Austin
-2013</a>; <a href="#ref-rubin1973" role="doc-biblioref">Rubin
+of each treated and control unit <span class="citation">(<a href="#ref-stuart2010">Stuart 2010</a>)</span>. Another popular distance
+is the Mahalanobis distance, described in the section “Mahalanobis
+distance matching” below. The order in which the treated units are to be
+paired must also be specified and has the potential to change the
+quality of the matches <span class="citation">(<a href="#ref-austin2013b">Austin 2013</a>; <a href="#ref-rubin1973">Rubin
1973</a>)</span>; this is specified by the <code>m.order</code>
argument. With propensity score matching, the default is to go in
descending order from the highest propensity score; doing so allows the
units that would have the hardest time finding close matches to be
-matched first <span class="citation">(<a href="#ref-rubin1973" role="doc-biblioref">Rubin 1973</a>)</span>. Other orderings are
-possible, including random ordering, which can be tried multiple times
-until an adequate matched sample is found. When matching with
-replacement (i.e., where each control unit can be reused to be matched
-with any number of treated units), the matching order doesn’t
-matter.</p>
+matched first <span class="citation">(<a href="#ref-rubin1973">Rubin
+1973</a>)</span>. Other orderings are possible, including random
+ordering, which can be tried multiple times until an adequate matched
+sample is found. When matching with replacement (i.e., where each
+control unit can be reused to be matched with any number of treated
+units), the matching order doesn’t matter.</p>
<p>When using a matching ratio greater than 1 (i.e., when more than 1
control units are requested to be matched to each treated unit),
matching occurs in a cycle, where each treated unit is first paired with
@@ -512,13 +511,14 @@ similar to nearest neighbor matching in that it attempts to pair each
treated unit with one or more control units. Unlike nearest neighbor
matching, however, it is “optimal” rather than greedy; it is optimal in
the sense that it attempts to choose matches that collectively optimize
-an overall criterion <span class="citation">(<a href="#ref-hansen2006" role="doc-biblioref">Hansen and Klopfer 2006</a>; <a href="#ref-gu1993" role="doc-biblioref">Gu and Rosenbaum 1993</a>)</span>. The criterion
-used is the sum of the absolute pair distances in the matched sample.
-See <code>?method_optimal</code> for the documentation for
+an overall criterion <span class="citation">(<a href="#ref-hansen2006">Hansen and Klopfer 2006</a>; <a href="#ref-gu1993">Gu and Rosenbaum 1993</a>)</span>. The criterion used
+is the sum of the absolute pair distances in the matched sample. See
+<code>?method_optimal</code> for the documentation for
<code>matchit()</code> with <code>method = "optimal"</code>. Optimal
pair matching in <code>MatchIt</code> depends on the
<code>fullmatch()</code> function in the <code>optmatch</code> package
-<span class="citation">(<a href="#ref-hansen2006" role="doc-biblioref">Hansen and Klopfer 2006</a>)</span>.</p>
+<span class="citation">(<a href="#ref-hansen2006">Hansen and Klopfer
+2006</a>)</span>.</p>
<p>Like nearest neighbor matching, optimal pair matching requires the
specification of a distance measure between units. Optimal pair matching
can be thought of simply as an alternative to selecting the order of the
@@ -526,7 +526,7 @@ matching for nearest neighbor matching. Optimal pair matching and
nearest neighbor matching often yield the same or very similar matched
samples; indeed, some research has indicated that optimal pair matching
is not much better than nearest neighbor matching at yielding balanced
-matched samples <span class="citation">(<a href="#ref-austin2013b" role="doc-biblioref">Austin 2013</a>)</span>.</p>
+matched samples <span class="citation">(<a href="#ref-austin2013b">Austin 2013</a>)</span>.</p>
<p>The <code>tol</code> argument in <code>fullmatch()</code> can be
supplied to <code>matchit()</code> with <code>method = "optimal"</code>;
this controls the numerical tolerance used to determine whether the
@@ -537,21 +537,19 @@ smaller problems, should be set much lower (e.g., by setting
<div id="optimal-full-matching-method-full" class="section level3">
<h3>Optimal Full Matching (<code>method = "full"</code>)</h3>
<p>Optimal full matching (often just called full matching) assigns every
-treated and control unit in the sample to one subclass each <span class="citation">(<a href="#ref-hansen2004" role="doc-biblioref">Hansen
-2004</a>; <a href="#ref-stuart2008a" role="doc-biblioref">Stuart and
-Green 2008</a>)</span>. Each subclass contains one treated unit and one
-or more control units or one control units and one or more treated
-units. It is optimal in the sense that the chosen number of subclasses
-and the assignment of units to subclasses minimize the sum of the
-absolute within-subclass distances in the matched sample. Weights are
-computed based on subclass membership, and these weights then function
-like propensity score weights and can be used to estimate a weighted
-treatment effect, ideally free of confounding by the measured
-covariates. See <code>?method_full</code> for the documentation for
-<code>matchit()</code> with <code>method = "full"</code>. Optimal full
-matching in <code>MatchIt</code> depends on the <code>fullmatch()</code>
-function in the <code>optmatch</code> package <span class="citation">(<a href="#ref-hansen2006" role="doc-biblioref">Hansen and Klopfer
-2006</a>)</span>.</p>
+treated and control unit in the sample to one subclass each <span class="citation">(<a href="#ref-hansen2004">Hansen 2004</a>; <a href="#ref-stuart2008a">Stuart and Green 2008</a>)</span>. Each subclass
+contains one treated unit and one or more control units or one control
+units and one or more treated units. It is optimal in the sense that the
+chosen number of subclasses and the assignment of units to subclasses
+minimize the sum of the absolute within-subclass distances in the
+matched sample. Weights are computed based on subclass membership, and
+these weights then function like propensity score weights and can be
+used to estimate a weighted treatment effect, ideally free of
+confounding by the measured covariates. See <code>?method_full</code>
+for the documentation for <code>matchit()</code> with
+<code>method = "full"</code>. Optimal full matching in
+<code>MatchIt</code> depends on the <code>fullmatch()</code> function in
+the <code>optmatch</code> package <span class="citation">(<a href="#ref-hansen2006">Hansen and Klopfer 2006</a>)</span>.</p>
<p>Like the other distance matching methods, optimal full matching
requires the specification of a distance measure between units. It can
be seen a combination of distance matching and stratum matching:
@@ -563,10 +561,10 @@ distance matching methods, full matching can be used to estimate the
ATE. Full matching can also be seen as a form of propensity score
weighting that is less sensitive to the form of the propensity score
model because the original propensity scores are used just to create the
-subclasses, not to form the weights directly <span class="citation">(<a href="#ref-austin2015a" role="doc-biblioref">Austin and Stuart
-2015a</a>)</span>. In addition, full matching does not have to rely on
-estimated propensity scores to form the subclasses and weights; other
-distance measures are allowed as well.</p>
+subclasses, not to form the weights directly <span class="citation">(<a href="#ref-austin2015a">Austin and Stuart 2015a</a>)</span>. In
+addition, full matching does not have to rely on estimated propensity
+scores to form the subclasses and weights; other distance measures are
+allowed as well.</p>
<p>Although full matching uses all available units, there is a loss in
precision due to the weights. Units may be weighted in such a way that
they contribute less to the sample than would unweighted units, so the
@@ -587,25 +585,26 @@ problems by setting, e.g., <code>tol = 1e-7</code>.</p>
<h3>Generalized Full Matching (<code>method = "quick"</code>)</h3>
<p>Generalized full matching is a variant of full matching that uses a
special fast clustering algorithm to dramatically speed up the matching,
-even for large datasets <span class="citation">(<a href="#ref-savjeGeneralizedFullMatching2021" role="doc-biblioref">Sävje,
-Higgins, and Sekhon 2021</a>)</span>. Like with optimal full matching,
-generalized full matching assigns every unit to a subclass. What makes
-generalized full match “generalized” is that the user can customize the
-matching in a number of ways, such as by specifying an arbitrary minimum
-number of units from each treatment group or total number of units per
-subclass, or by allowing not all units from a treatment group to have to
-be matched. Generalized full matching minimizes the largest
-within-subclass distances in the matched sample, but it does so in a way
-that is not completely optimal (though the solution is often very close
-to the optimal solution). Matching weights are computed based on
-subclass membership, and these weights then function like propensity
-score weights and can be used to estimate a weighted treatment effect,
-ideally free of confounding by the measured covariates. See
+even for large datasets <span class="citation">(<a href="#ref-savjeGeneralizedFullMatching2021">Sävje, Higgins, and Sekhon
+2021</a>)</span>. Like with optimal full matching, generalized full
+matching assigns every unit to a subclass. What makes generalized full
+match “generalized” is that the user can customize the matching in a
+number of ways, such as by specifying an arbitrary minimum number of
+units from each treatment group or total number of units per subclass,
+or by allowing not all units from a treatment group to have to be
+matched. Generalized full matching minimizes the largest within-subclass
+distances in the matched sample, but it does so in a way that is not
+completely optimal (though the solution is often very close to the
+optimal solution). Matching weights are computed based on subclass
+membership, and these weights then function like propensity score
+weights and can be used to estimate a weighted treatment effect, ideally
+free of confounding by the measured covariates. See
<code>?method_quick</code> for the documentation for
<code>matchit()</code> with <code>method = "quick"</code>. Generalized
full matching in <code>MatchIt</code> depends on the
<code>quickmatch()</code> function in the <code>quickmatch</code>
-package <span class="citation">(<a href="#ref-savjeQuickmatchQuickGeneralized2018" role="doc-biblioref">Savje, Sekhon, and Higgins 2018</a>)</span>.</p>
+package <span class="citation">(<a href="#ref-savjeQuickmatchQuickGeneralized2018">Savje, Sekhon, and
+Higgins 2018</a>)</span>.</p>
<p>Generalized full matching includes different options for
customization than optimal full matching. The user cannot supply their
own distance matrix, but propensity scores and distance metrics that are
@@ -626,17 +625,16 @@ practice, though, the form of matching used is nearest neighbor pair
matching. Genetic matching uses a genetic algorithm, which is an
optimization routine used for non-differentiable objective functions, to
find scaling factors for each variable in a generalized Mahalanobis
-distance formula <span class="citation">(<a href="#ref-diamond2013" role="doc-biblioref">Diamond and Sekhon 2013</a>)</span>. The criterion
-optimized by the algorithm is one based on covariate balance. Once the
-scaling factors have been found, nearest neighbor matching is performed
-on the scaled generalized Mahalanobis distance. See
+distance formula <span class="citation">(<a href="#ref-diamond2013">Diamond and Sekhon 2013</a>)</span>. The
+criterion optimized by the algorithm is one based on covariate balance.
+Once the scaling factors have been found, nearest neighbor matching is
+performed on the scaled generalized Mahalanobis distance. See
<code>?method_genetic</code> for the documentation for
<code>matchit()</code> with <code>method = "genetic"</code>. Genetic
matching in <code>MatchIt</code> depends on the <code>GenMatch()</code>
-function in the <code>Matching</code> package <span class="citation">(<a href="#ref-sekhon2011" role="doc-biblioref">Sekhon 2011</a>)</span> to
-perform the genetic search and uses the <code>Match()</code> function to
-perform the nearest neighbor match using the scaled generalized
-Mahalanobis distance.</p>
+function in the <code>Matching</code> package <span class="citation">(<a href="#ref-sekhon2011">Sekhon 2011</a>)</span> to perform the genetic
+search and uses the <code>Match()</code> function to perform the nearest
+neighbor match using the scaled generalized Mahalanobis distance.</p>
<p>Genetic matching considers the generalized Mahalanobis distance
between a treated unit <span class="math inline">\(i\)</span> and a
control unit <span class="math inline">\(j\)</span> as <span class="math display">\[\delta_{GMD}(\mathbf{x}_i,\mathbf{x}_j,
@@ -716,17 +714,17 @@ done.</p>
<p>Coarsened exact matching (CEM) is a form of stratum matching that
involves first coarsening the covariates by creating bins and then
performing exact matching on the new coarsened versions of the
-covariates <span class="citation">(<a href="#ref-iacus2012" role="doc-biblioref">Iacus, King, and Porro 2012</a>)</span>. The degree
-and method of coarsening can be controlled by the user to manage the
-trade-off between exact and approximate balancing. For example,
-coarsening a covariate to two bins will mean that units that differ
-greatly on the covariate might be placed into the same subclass, while
-coarsening a variable to five bins may require units to be dropped due
-to not finding matches. Like exact matching, CEM is susceptible to the
-curse of dimensionality, making it a less viable solution with many
-covariates, especially with few units. Dropping units can also change
-the target population of the estimated effect. See
-<code>?method_cem</code> for the documentation for
+covariates <span class="citation">(<a href="#ref-iacus2012">Iacus, King,
+and Porro 2012</a>)</span>. The degree and method of coarsening can be
+controlled by the user to manage the trade-off between exact and
+approximate balancing. For example, coarsening a covariate to two bins
+will mean that units that differ greatly on the covariate might be
+placed into the same subclass, while coarsening a variable to five bins
+may require units to be dropped due to not finding matches. Like exact
+matching, CEM is susceptible to the curse of dimensionality, making it a
+less viable solution with many covariates, especially with few units.
+Dropping units can also change the target population of the estimated
+effect. See <code>?method_cem</code> for the documentation for
<code>matchit()</code> with <code>method = "cem"</code>. CEM in
<code>MatchIt</code> does not depend on any other package to perform the
coarsening and matching, though it used to rely on the <code>cem</code>
@@ -741,10 +739,9 @@ quantiles of the propensity score distribution either in the treated
group, control group, or overall, depending on the desired estimand.
Propensity score subclassification is an old and well-studied method,
though it can perform poorly compared to other, more modern propensity
-score methods such as full matching and weighting <span class="citation">(<a href="#ref-austin2010" role="doc-biblioref">Austin
-2010a</a>)</span>. See <code>?method_subclass</code> for the
-documentation for <code>matchit()</code> with
-<code>method = "subclass"</code>.</p>
+score methods such as full matching and weighting <span class="citation">(<a href="#ref-austin2010">Austin 2010a</a>)</span>.
+See <code>?method_subclass</code> for the documentation for
+<code>matchit()</code> with <code>method = "subclass"</code>.</p>
<p>The binning of the propensity scores is typically based on dividing
the distribution of covariates into approximately equally sized bins.
The user specifies the number of subclasses using the
@@ -763,7 +760,7 @@ but can increase precision.</p>
not be taken as a recommended value. Although early theory has
recommended the use of 5 subclasses, in general there is an optimal
number of subclasses that is typically much larger than 5 but that
-varies among datasets <span class="citation">(<a href="#ref-orihara2021" role="doc-biblioref">Orihara and Hamada 2021</a>)</span>. Rather than
+varies among datasets <span class="citation">(<a href="#ref-orihara2021">Orihara and Hamada 2021</a>)</span>. Rather than
trying to figure this out for oneself, one can use optimal full matching
(i.e., with <code>method = "full"</code>) or generalized full matching
(<code>method = "quick"</code>) to optimally create subclasses that
@@ -773,8 +770,8 @@ assigned subclasses and the subclassification weights. Effects can be
estimated either within each subclass and then averaged across them, or
a single marginal effect can be estimated using the subclassification
weights. This latter method has been called marginal mean weighting
-through subclassification [MMWS; <span class="citation">Hong (<a href="#ref-hong2010" role="doc-biblioref">2010</a>)</span>] and fine
-stratification weighting <span class="citation">(<a href="#ref-desai2017" role="doc-biblioref">Desai et al.
+through subclassification [MMWS; <span class="citation">Hong (<a href="#ref-hong2010">2010</a>)</span>] and fine stratification weighting
+<span class="citation">(<a href="#ref-desai2017">Desai et al.
2017</a>)</span>. It is also implemented in the <code>WeightIt</code>
package.</p>
</div>
@@ -788,18 +785,18 @@ pairs or subclasses. They can be thought of as a weighting method where
the weights are restricted to be zero or one. Cardinality matching
involves finding the largest sample that satisfies user-supplied balance
constraints and constraints on the ratio of matched treated to matched
-control units <span class="citation">(<a href="#ref-zubizarretaMatchingBalancePairing2014" role="doc-biblioref">Zubizarreta, Paredes, and Rosenbaum
-2014b</a>)</span>. It does not consider a specific estimand and can be a
-useful alternative to matching with a caliper for handling data with
-little overlap <span class="citation">(<a href="#ref-visconti2018" role="doc-biblioref">Visconti and Zubizarreta 2018</a>)</span>. Profile
-matching involves identifying a target distribution (e.g., the full
-sample for the ATE or the treated units for the ATT) and finding the
-largest subset of the treated and control groups that satisfy
-user-supplied balance constraints with respect to that target <span class="citation">(<a href="#ref-cohnProfileMatchingGeneralization2021" role="doc-biblioref">Cohn and Zubizarreta 2021</a>)</span>. See
-<code>?method_cardinality</code> for the documentation for using
-<code>matchit()</code> with <code>method = "cardinality"</code>,
-including which inputs are required to request either cardinality
-matching or profile matching.</p>
+control units <span class="citation">(<a href="#ref-zubizarretaMatchingBalancePairing2014">Zubizarreta, Paredes,
+and Rosenbaum 2014b</a>)</span>. It does not consider a specific
+estimand and can be a useful alternative to matching with a caliper for
+handling data with little overlap <span class="citation">(<a href="#ref-visconti2018">Visconti and Zubizarreta 2018</a>)</span>.
+Profile matching involves identifying a target distribution (e.g., the
+full sample for the ATE or the treated units for the ATT) and finding
+the largest subset of the treated and control groups that satisfy
+user-supplied balance constraints with respect to that target <span class="citation">(<a href="#ref-cohnProfileMatchingGeneralization2021">Cohn and Zubizarreta
+2021</a>)</span>. See <code>?method_cardinality</code> for the
+documentation for using <code>matchit()</code> with
+<code>method = "cardinality"</code>, including which inputs are required
+to request either cardinality matching or profile matching.</p>
<p>Subset selection is performed by solving a mixed integer programming
optimization problem with linear constraints. The problem involves
maximizing the size of the matched sample subject to constraints on
@@ -819,8 +816,8 @@ the <code>tols</code> and <code>std.tols</code> arguments. One can also
create pairs in the matched sample by using the <code>mahvars</code>
argument, which requests that optimal Mahalanobis matching be done after
subset selection; doing so can add additional precision and robustness
-<span class="citation">(<a href="#ref-zubizarretaMatchingBalancePairing2014" role="doc-biblioref">Zubizarreta, Paredes, and Rosenbaum
-2014b</a>)</span>.</p>
+<span class="citation">(<a href="#ref-zubizarretaMatchingBalancePairing2014">Zubizarreta, Paredes,
+and Rosenbaum 2014b</a>)</span>.</p>
<p>The optimization problem requires a special solver to solve.
Currently, the available options in <code>MatchIt</code> are the GLPK
solver (through the <code>Rglpk</code> package), the SYMPHONY solver
@@ -856,11 +853,11 @@ propensity score. In <code>MatchIt</code>, propensity scores are often
labeled as “distance” values, even though the propensity score itself is
not a distance measure. This is to reflect that the propensity score is
used in creating the distance value, but other scores could be used,
-such as prognostic scores for prognostic score matching <span class="citation">(<a href="#ref-hansen2008a" role="doc-biblioref">Hansen
-2008</a>)</span>. The propensity score is more like a “position” value,
-in that it reflects the position of each unit in the matching space, and
-the difference between positions is the distance between them. If the
-the argument to <code>distance</code> is one of the allowed methods for
+such as prognostic scores for prognostic score matching <span class="citation">(<a href="#ref-hansen2008a">Hansen 2008</a>)</span>.
+The propensity score is more like a “position” value, in that it
+reflects the position of each unit in the matching space, and the
+difference between positions is the distance between them. If the the
+argument to <code>distance</code> is one of the allowed methods for
estimating propensity scores (see <code>?distance</code> for these
values) or is a numeric vector with one value per unit, the distance
between units will be computed as the pairwise difference between
@@ -876,14 +873,13 @@ generalized linear model. The <code>link</code> and
specify the options for the propensity score models, including whether
to use the raw propensity score or a linearized version of it (e.g., the
logit of a logistic regression propensity score, which has been commonly
-referred to and recommended in the propensity score literature <span class="citation">(<a href="#ref-austin2011a" role="doc-biblioref">Austin
-2011</a>; <a href="#ref-stuart2010" role="doc-biblioref">Stuart
-2010</a>)</span>). Allowable options for the propensity score model
-include parametric and machine learning-based models, each of which have
-their strengths and limitations and may perform differently depending on
-the unique qualities of each dataset. We recommend multiple types of
-models be tried to find one that yields the best balance, as there is no
-way to make a single recommendation that will work for all cases.</p>
+referred to and recommended in the propensity score literature <span class="citation">(<a href="#ref-austin2011a">Austin 2011</a>; <a href="#ref-stuart2010">Stuart 2010</a>)</span>). Allowable options for
+the propensity score model include parametric and machine learning-based
+models, each of which have their strengths and limitations and may
+perform differently depending on the unique qualities of each dataset.
+We recommend multiple types of models be tried to find one that yields
+the best balance, as there is no way to make a single recommendation
+that will work for all cases.</p>
<p>The <code>distance</code> argument can also be specified as a method
of computing pairwise distances from the covariates directly (i.e.,
without estimating propensity scores). The options include
@@ -897,19 +893,21 @@ for that unit, <span class="math inline">\(S\)</span> is a scaling
matrix, and <span class="math inline">\(S^{-1}\)</span> is the
(generalized) inverse of <span class="math inline">\(S\)</span>. For
Mahalanobis distance matching, <span class="math inline">\(S\)</span> is
-the pooled covariance matrix of the covariates <span class="citation">(<a href="#ref-rubinBiasReductionUsing1980" role="doc-biblioref">Rubin 1980</a>)</span>; for Euclidean distance
-matching, <span class="math inline">\(S\)</span> is the identity matrix
-(i.e., no scaling); and for scaled Euclidean distance matching, <span class="math inline">\(S\)</span> is the diagonal of the pooled
+the pooled covariance matrix of the covariates <span class="citation">(<a href="#ref-rubinBiasReductionUsing1980">Rubin
+1980</a>)</span>; for Euclidean distance matching, <span class="math inline">\(S\)</span> is the identity matrix (i.e., no
+scaling); and for scaled Euclidean distance matching, <span class="math inline">\(S\)</span> is the diagonal of the pooled
covariance matrix (containing just the variances). The robust
Mahalanobis distance is computed not on the covariates directly but
-rather on their ranks and uses a correction for ties (see <span class="citation">Rosenbaum (<a href="#ref-rosenbaumDesignObservationalStudies2010" role="doc-biblioref">2010</a>)</span>, ch 8). For creating close pairs,
-matching with these distance measures tends work better than propensity
-score matching because paired units will have close values on all of the
-covariates, whereas propensity score-paired units may be close on the
-propensity score but not on any of the covariates themselves. This
-feature was the basis of King and Nielsen’s <span class="citation">(<a href="#ref-king2019" role="doc-biblioref">2019</a>)</span> warning
-against using propensity scores for matching. That said, they do not
-always outperform propensity score matching <span class="citation">(<a href="#ref-ripolloneImplicationsPropensityScore2018" role="doc-biblioref">Ripollone et al. 2018</a>)</span>.</p>
+rather on their ranks and uses a correction for ties (see <span class="citation">Rosenbaum (<a href="#ref-rosenbaumDesignObservationalStudies2010">2010</a>)</span>, ch
+8). For creating close pairs, matching with these distance measures
+tends work better than propensity score matching because paired units
+will have close values on all of the covariates, whereas propensity
+score-paired units may be close on the propensity score but not on any
+of the covariates themselves. This feature was the basis of King and
+Nielsen’s <span class="citation">(<a href="#ref-king2019">2019</a>)</span> warning against using propensity
+scores for matching. That said, they do not always outperform propensity
+score matching <span class="citation">(<a href="#ref-ripolloneImplicationsPropensityScore2018">Ripollone et al.
+2018</a>)</span>.</p>
<p><code>distance</code> can also be supplied as a matrix of distance
values between units. This makes it possible to use handcrafted distance
matrices or distances created outside <code>MatchIt</code>. Only nearest
@@ -1002,10 +1000,10 @@ dimensionality. As with exact matching performed with
<code>method = "exact"</code>, any units in strata lacking members of
one of the treatment groups will be left unmatched. Note that although
matching occurs within each exact matching stratum, propensity score
-estimation and computation of the Mahalanobis distance occur in the full
-sample. <strong>If units from the treated group are unmatched due to an
-exact matching restriction, the estimand no longer corresponds to the
-ATT.</strong></p>
+estimation and computation of the Mahalanobis or other distance matrix
+occur in the full sample. <strong>If units from the treated group are
+unmatched due to an exact matching restriction, the estimand no longer
+corresponds to the ATT.</strong></p>
</div>
<div id="anti-exact-matching-antiexact" class="section level3">
<h3>Anti-exact matching (<code>antiexact</code>)</h3>
@@ -1028,20 +1026,19 @@ multiple treated units. Matching without replacement carries certain
statistical benefits in that weights for each unit can be omitted or are
more straightforward to include and dependence between units depends
only on pair membership. Special standard error estimators are sometimes
-required for estimating effects after matching with replacement <span class="citation">(<a href="#ref-austin2020a" role="doc-biblioref">Austin
-and Cafri 2020</a>)</span>, and methods for accounting for uncertainty
-are not well understood for non-continuous outcomes. Matching with
-replacement will tend to yield better balance though, because the
-problem of “running out” of close control units to match to treated
-units is avoided, though the reuse of control units will decrease the
-effect sample size, thereby worsening precision <span class="citation">(<a href="#ref-austin2013b" role="doc-biblioref">Austin
-2013</a>)</span>. (This problem occurs in the Lalonde dataset used in
-<code>vignette("MatchIt")</code>, which is why nearest neighbor matching
-without replacement is not very effective there.) After matching with
-replacement, control units are assigned to more than one subclass, so
-the <code>get_matches()</code> function should be used instead of
-<code>match.data()</code> after matching with replacement if subclasses
-are to be used in follow-up analyses; see
+required for estimating effects after matching with replacement <span class="citation">(<a href="#ref-austin2020a">Austin and Cafri
+2020</a>)</span>, and methods for accounting for uncertainty are not
+well understood for non-continuous outcomes. Matching with replacement
+will tend to yield better balance though, because the problem of
+“running out” of close control units to match to treated units is
+avoided, though the reuse of control units will decrease the effect
+sample size, thereby worsening precision <span class="citation">(<a href="#ref-austin2013b">Austin 2013</a>)</span>. (This problem occurs in
+the Lalonde dataset used in <code>vignette("MatchIt")</code>, which is
+why nearest neighbor matching without replacement is not very effective
+there.) After matching with replacement, control units are assigned to
+more than one subclass, so the <code>get_matches()</code> function
+should be used instead of <code>match.data()</code> after matching with
+replacement if subclasses are to be used in follow-up analyses; see
<code>vignette("estimating-effects")</code> for details.</p>
<p>The <code>reuse.max</code> argument can also be used with
<code>method = "nearest"</code> to control how many times each control
@@ -1062,21 +1059,48 @@ Performing <span class="math inline">\(k\)</span>:1 matching can
preserve precision by preventing too many control units from being
unmatched and dropped from the matched sample, though the gain in
precision by increasing <span class="math inline">\(k\)</span>
-diminishes rapidly after 4 <span class="citation">(<a href="#ref-rosenbaum2020" role="doc-biblioref">Rosenbaum
-2020</a>)</span>. Importantly, for <span class="math inline">\(k>1\)</span>, the matches after the first match
-will generally be worse than the first match in terms of closeness to
-the treated unit, so increasing <span class="math inline">\(k\)</span>
-can also worsen balance. <span class="citation">Austin (<a href="#ref-austin2010a" role="doc-biblioref">2010b</a>)</span> found
-that 1:1 or 1:2 matching generally performed best in terms of mean
+diminishes rapidly after 4 <span class="citation">(<a href="#ref-rosenbaum2020">Rosenbaum 2020</a>)</span>. Importantly, for
+<span class="math inline">\(k>1\)</span>, the matches after the first
+match will generally be worse than the first match in terms of closeness
+to the treated unit, so increasing <span class="math inline">\(k\)</span> can also worsen balance. <span class="citation">Austin (<a href="#ref-austin2010a">2010b</a>)</span>
+found that 1:1 or 1:2 matching generally performed best in terms of mean
squared error. In general, it makes sense to use higher values of <span class="math inline">\(k\)</span> while ensuring that balance is
satisfactory.</p>
<p>With nearest neighbor and optimal pair matching, variable <span class="math inline">\(k\)</span>:1 matching, in which the number of
controls matched to each treated unit varies, can also be used; this can
-have improved performance over “fixed” <span class="math inline">\(k\)</span>:1 matching <span class="citation">(<a href="#ref-ming2000" role="doc-biblioref">Ming and Rosenbaum
-2000</a>)</span>. See <code>?method_nearest</code> and
+have improved performance over “fixed” <span class="math inline">\(k\)</span>:1 matching <span class="citation">(<a href="#ref-ming2000">Ming and Rosenbaum 2000</a>; <a href="#ref-rassenOnetomanyPropensityScore2012">Rassen et al.
+2012</a>)</span>. See <code>?method_nearest</code> and
<code>?method_optimal</code> for information on implementing variable
<span class="math inline">\(k\)</span>:1 matching.</p>
</div>
+<div id="matching-order-m.order" class="section level3">
+<h3>Matching order (<code>m.order</code>)</h3>
+<p>For nearest neighbor matching (including genetic matching), units are
+matched in an order, and that order can affect the quality of individual
+matches and of the resulting matched sample. With
+<code>method = "nearest"</code>, the allowable options to
+<code>m.order</code> to control the matching order are
+<code>"largest"</code>, <code>"smallest"</code>, <code>"closest"</code>,
+<code>"random"</code>, and <code>"data"</code>. With
+<code>method = "genetic"</code>, all but <code>"closest"</code> can be
+used. Requesting <code>"largest"</code> means that treated units with
+the largest propensity scores, i.e., those least like the control units,
+will be matched first, which prevents them from having bad matches after
+all the close control units have been used up. <code>"smallest"</code>
+means that treated units with the smallest propensity scores are matched
+first. <code>"closest"</code> means that potential pairs with the
+smallest distance between units will be matched first, which ensures
+that the best possible matches are included in the matched sample but
+can yield poor matches for units whose best match is far from them; this
+makes it particularly useful when matching with a caliper.
+<code>"random"</code> matches in a random order and <code>"data"</code>
+matches in order of the data. A propensity score is required for
+<code>"largest"</code> and <code>"smallest"</code> but not for the other
+options. <span class="citation">Rubin (<a href="#ref-rubin1973">1973</a>)</span> recommends using
+<code>"largest"</code> or <code>"random"</code>, though <span class="citation">Austin (<a href="#ref-austin2013b">2013</a>)</span>
+recommends against <code>"largest"</code> and instead favors
+<code>"closest"</code> or <code>"random"</code>.</p>
+</div>
</div>
<div id="choosing-a-matching-method" class="section level2">
<h2>Choosing a Matching Method</h2>
@@ -1100,11 +1124,11 @@ another, until a satisfactory specification has been found. It is
important to assess balance broadly (i.e., beyond comparing the means of
the covariates in the treated and control groups), and the search for a
matching specification should not stop when a threshold is reached, but
-should attempt to come as close as possible to perfect balance <span class="citation">(<a href="#ref-ho2007" role="doc-biblioref">Ho et al.
-2007</a>)</span>. Even if the first matching specification appears
-successful at reducing imbalance, there may be another specification
-that could reduce it even further, thereby increasing the robustness of
-the inference and the plausibility of an unbiased effect estimate.</p>
+should attempt to come as close as possible to perfect balance <span class="citation">(<a href="#ref-ho2007">Ho et al. 2007</a>)</span>. Even
+if the first matching specification appears successful at reducing
+imbalance, there may be another specification that could reduce it even
+further, thereby increasing the robustness of the inference and the
+plausibility of an unbiased effect estimate.</p>
<p>If the target of inference is the ATE, optimal or generalized full
matching, subclassification, or profile matching can be used. If the
target of inference is the ATT or ATC, any matching method may be used.
@@ -1170,36 +1194,38 @@ of each dataset.</p>
engaging in treatment effect discovery or when when the sampled
population is not of particular interest (e.g., it corresponds to an
arbitrarily chosen hospital or school; see <span class="citation">Mao,
-Li, and Greene (<a href="#ref-mao2018" role="doc-biblioref">2018</a>)</span> for these and other reasons why
-retaining the target population may not be important), other methods
-that do not retain the characteristics of the original sample become
-available. These include matching with a caliper (on the propensity
-score or on the covariates themselves), cardinality matching, and more
-restrictive forms of matching like exact and coarsened exact matching,
-either on all covariates or just a subset, that are prone to discard
-units from the sample in such a way that the target population is
-changed. <span class="citation">Austin (<a href="#ref-austin2013b" role="doc-biblioref">2013</a>)</span> and Austin and Stuart <span class="citation">(<a href="#ref-austin2015c" role="doc-biblioref">2015b</a>, <a href="#ref-austin2015a" role="doc-biblioref">2015a</a>)</span> have found that caliper matching
-can be a particularly effective modification to nearest neighbor
-matching for eliminating imbalance and reducing bias when the target
-population is less relevant, but when inference to a specific target
-population is desired, using calipers can induce bias due to incomplete
-matching <span class="citation">(<a href="#ref-rosenbaum1985" role="doc-biblioref">Rosenbaum and Rubin 1985a</a>; <a href="#ref-wang2020" role="doc-biblioref">Wang 2020</a>)</span>.
-Cardinality matching can be particularly effective in data with little
-overlap between the treatment groups <span class="citation">(<a href="#ref-visconti2018" role="doc-biblioref">Visconti and Zubizarreta
-2018</a>)</span> and can perform better than caliper matching <span class="citation">(<a href="#ref-delosangelesresaDirectStableWeight2020" role="doc-biblioref">de los Angeles Resa and Zubizarreta
-2020</a>)</span>.</p>
+Li, and Greene (<a href="#ref-mao2018">2018</a>)</span> for these and
+other reasons why retaining the target population may not be important),
+other methods that do not retain the characteristics of the original
+sample become available. These include matching with a caliper (on the
+propensity score or on the covariates themselves), cardinality matching,
+and more restrictive forms of matching like exact and coarsened exact
+matching, either on all covariates or just a subset, that are prone to
+discard units from the sample in such a way that the target population
+is changed. <span class="citation">Austin (<a href="#ref-austin2013b">2013</a>)</span> and Austin and Stuart <span class="citation">(<a href="#ref-austin2015c">2015b</a>, <a href="#ref-austin2015a">2015a</a>)</span> have found that caliper
+matching can be a particularly effective modification to nearest
+neighbor matching for eliminating imbalance and reducing bias when the
+target population is less relevant, but when inference to a specific
+target population is desired, using calipers can induce bias due to
+incomplete matching <span class="citation">(<a href="#ref-rosenbaum1985">Rosenbaum and Rubin 1985a</a>; <a href="#ref-wang2020">Wang 2020</a>)</span>. Cardinality matching can be
+particularly effective in data with little overlap between the treatment
+groups <span class="citation">(<a href="#ref-visconti2018">Visconti and
+Zubizarreta 2018</a>)</span> and can perform better than caliper
+matching <span class="citation">(<a href="#ref-delosangelesresaDirectStableWeight2020">de los Angeles Resa
+and Zubizarreta 2020</a>)</span>.</p>
<p>It is important not to rely excessively on theoretical or
simulation-based findings or specific recommendations when making
choices about the best matching method to use. For example, although
nearest neighbor matching without replacement balance covariates better
-than did subclassification with five or ten subclasses in Austin’s <span class="citation">(<a href="#ref-austin2009c" role="doc-biblioref">2009</a>)</span> simulation, this does not imply it
-will be superior in all datasets. Likewise, though <span class="citation">Rosenbaum and Rubin (<a href="#ref-rosenbaum1985a" role="doc-biblioref">1985b</a>)</span> and <span class="citation">Austin
-(<a href="#ref-austin2011a" role="doc-biblioref">2011</a>)</span> both
-recommend using a caliper of .2 standard deviations of the logit of the
-propensity score, this does not imply that caliper will be optimal in
-all scenarios, and other widths should be tried, though it should be
+than did subclassification with five or ten subclasses in Austin’s <span class="citation">(<a href="#ref-austin2009c">2009</a>)</span>
+simulation, this does not imply it will be superior in all datasets.
+Likewise, though <span class="citation">Rosenbaum and Rubin (<a href="#ref-rosenbaum1985a">1985b</a>)</span> and <span class="citation">Austin (<a href="#ref-austin2011a">2011</a>)</span>
+both recommend using a caliper of .2 standard deviations of the logit of
+the propensity score, this does not imply that caliper will be optimal
+in all scenarios, and other widths should be tried, though it should be
noted that tightening the caliper on the propensity score can sometimes
-degrade performance <span class="citation">(<a href="#ref-king2019" role="doc-biblioref">King and Nielsen 2019</a>)</span>.</p>
+degrade performance <span class="citation">(<a href="#ref-king2019">King
+and Nielsen 2019</a>)</span>.</p>
<p>For large datasets (i.e., in 10,000s to millions), some matching
methods will be too slow to be used at scale. Instead, users should
consider generalized full matching, subclassification, or coarsened
@@ -1375,6 +1401,12 @@ Orihara, Shunichiro, and Etsuo Hamada. 2021. <span>“Determination of the
Optimal Number of Strata for Propensity Score Subclassification.”</span>
<em>Statistics & Probability Letters</em> 168 (January): 108951. <a href="https://doi.org/10.1016/j.spl.2020.108951">https://doi.org/10.1016/j.spl.2020.108951</a>.
</div>
+<div id="ref-rassenOnetomanyPropensityScore2012" class="csl-entry">
+Rassen, Jeremy A., Abhi A. Shelat, Jessica Myers, Robert J. Glynn,
+Kenneth J. Rothman, and Sebastian Schneeweiss. 2012. <span>“One-to-Many
+Propensity Score Matching in Cohort Studies.”</span>
+<em>Pharmacoepidemiology and Drug Safety</em> 21 (S2): 69–80. <a href="https://doi.org/10.1002/pds.3263">https://doi.org/10.1002/pds.3263</a>.
+</div>
<div id="ref-ripolloneImplicationsPropensityScore2018" class="csl-entry">
Ripollone, John E., Krista F. Huybrechts, Kenneth J. Rothman, Ryan E.
Ferguson, and Jessica M. Franklin. 2018. <span>“Implications of the
diff --git a/inst/doc/sampling-weights.html b/inst/doc/sampling-weights.html
index 3145a74..4760a02 100644
--- a/inst/doc/sampling-weights.html
+++ b/inst/doc/sampling-weights.html
@@ -12,7 +12,7 @@
<meta name="author" content="Noah Greifer" />
-<meta name="date" content="2023-02-22" />
+<meta name="date" content="2023-06-13" />
<title>Matching with Sampling Weights</title>
@@ -362,7 +362,7 @@ code > span.er { color: #a61717; background-color: #e3d2d2; }
<h1 class="title toc-ignore">Matching with Sampling Weights</h1>
<h4 class="author">Noah Greifer</h4>
-<h4 class="date">2023-02-22</h4>
+<h4 class="date">2023-06-13</h4>
<div id="TOC">
@@ -395,8 +395,9 @@ any estimated quantities generalize to a target population defined by
the weights. Evidence suggests that sampling weights need to be
incorporated into a propensity score matching analysis to obtain valid
and unbiased estimates of the treatment effect in the sampling weighted
-population <span class="citation">(<a href="#ref-dugoff2014" role="doc-biblioref">DuGoff, Schuler, and Stuart 2014</a>; <a href="#ref-austin2016" role="doc-biblioref">Austin, Jembere, and Chiu
-2016</a>; <a href="#ref-lenis2019" role="doc-biblioref">Lenis et al.
+population <span class="citation">(<a href="#ref-dugoff2014">DuGoff,
+Schuler, and Stuart 2014</a>; <a href="#ref-austin2016">Austin, Jembere,
+and Chiu 2016</a>; <a href="#ref-lenis2019">Lenis et al.
2019</a>)</span>. In this guide, we demonstrate how to use sampling
weights with <code>MatchIt</code> for propensity score estimation,
balance assessment, and effect estimation. Fortunately, doing so is not
@@ -416,7 +417,7 @@ sampling weights is at the end of this document. We will consider the
effect of binary treatment <code>A</code> on continuous outcome
<code>Y_C</code>, adjusting for confounders
<code>X1</code>-<code>X9</code>.</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(d)</span></code></pre></div>
+<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">head</span>(d)</span></code></pre></div>
<pre><code>## A X1 X2 X3 X4 X5 X6 X7 X8 X9 Y_C SW
## 1 0 0.1725 -1.4283 -0.4103 -2.36059 1 -1.1199 0.6398 -0.4840 -0.59385 -3.5907 1.675
## 2 0 -1.0959 0.8463 0.2456 -0.12333 1 -2.2687 -1.4491 -0.5514 -0.31439 -1.5481 1.411
@@ -424,18 +425,18 @@ effect of binary treatment <code>A</code> on continuous outcome
## 4 0 -0.4595 0.1726 1.9542 -0.62661 1 -0.4019 -0.8294 -0.5384 0.20729 2.4906 1.644
## 5 1 0.3563 -1.8121 0.8135 -0.67189 1 -0.8297 1.7297 -0.6439 -0.02648 -0.6687 2.722
## 6 0 -2.4313 -1.7984 -1.2940 0.04609 1 -1.2419 -1.1252 -1.8659 -0.56513 -9.8504 14.773</code></pre>
-<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"MatchIt"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"MatchIt"</span>)</span></code></pre></div>
</div>
<div id="matching" class="section level2">
<h2>Matching</h2>
<p>When using sampling weights with propensity score matching, one has
the option of including the sampling weights in the model used to
estimate the propensity scores. Although evidence is mixed on whether
-this is required <span class="citation">(<a href="#ref-austin2016" role="doc-biblioref">Austin, Jembere, and Chiu 2016</a>; <a href="#ref-lenis2019" role="doc-biblioref">Lenis et al.
-2019</a>)</span>, it can be a good idea. The choice should depend on
-whether including the sampling weights improves the quality of the
-matches. Specifications including and excluding sampling weights should
-be tried to determine which is preferred.</p>
+this is required <span class="citation">(<a href="#ref-austin2016">Austin, Jembere, and Chiu 2016</a>; <a href="#ref-lenis2019">Lenis et al. 2019</a>)</span>, it can be a good
+idea. The choice should depend on whether including the sampling weights
+improves the quality of the matches. Specifications including and
+excluding sampling weights should be tried to determine which is
+preferred.</p>
<p>To supply sampling weights to the propensity score-estimating
function in <code>matchit()</code>, the sampling weights variable should
be supplied to the <code>s.weights</code> argument. It can be supplied
@@ -446,11 +447,11 @@ sampling weights into propensity scores estimated using logistic
regression for optimal full matching for the average treatment effect in
the population (ATE) (note that all methods and steps apply the same way
to all forms of matching and all estimands).</p>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>mF_s <span class="ot"><-</span> <span class="fu">matchit</span>(A <span class="sc">~</span> X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
-<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9, <span class="at">data =</span> d,</span>
-<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a> <span class="at">method =</span> <span class="st">"full"</span>, <span class="at">distance =</span> <span class="st">"glm"</span>,</span>
-<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a> <span class="at">estimand =</span> <span class="st">"ATE"</span>, <span class="at">s.weights =</span> <span class="sc">~</span>SW)</span>
-<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>mF_s</span></code></pre></div>
+<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a>mF_s <span class="ot"><-</span> <span class="fu">matchit</span>(A <span class="sc">~</span> X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
+<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9, <span class="at">data =</span> d,</span>
+<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a> <span class="at">method =</span> <span class="st">"full"</span>, <span class="at">distance =</span> <span class="st">"glm"</span>,</span>
+<span id="cb4-4"><a href="#cb4-4" tabindex="-1"></a> <span class="at">estimand =</span> <span class="st">"ATE"</span>, <span class="at">s.weights =</span> <span class="sc">~</span>SW)</span>
+<span id="cb4-5"><a href="#cb4-5" tabindex="-1"></a>mF_s</span></code></pre></div>
<pre><code>## A matchit object
## - method: Optimal full matching
## - distance: Propensity score
@@ -473,11 +474,11 @@ they are calculated simply as a result of the matching.</p>
include the sampling weights in its estimation. Here we use the same
specification as was used in
<code>vignette("estimating-effects")</code>.</p>
-<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>mF <span class="ot"><-</span> <span class="fu">matchit</span>(A <span class="sc">~</span> X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
-<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9, <span class="at">data =</span> d,</span>
-<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a> <span class="at">method =</span> <span class="st">"full"</span>, <span class="at">distance =</span> <span class="st">"glm"</span>,</span>
-<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a> <span class="at">estimand =</span> <span class="st">"ATE"</span>)</span>
-<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>mF</span></code></pre></div>
+<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>mF <span class="ot"><-</span> <span class="fu">matchit</span>(A <span class="sc">~</span> X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
+<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9, <span class="at">data =</span> d,</span>
+<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a> <span class="at">method =</span> <span class="st">"full"</span>, <span class="at">distance =</span> <span class="st">"glm"</span>,</span>
+<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a> <span class="at">estimand =</span> <span class="st">"ATE"</span>)</span>
+<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a>mF</span></code></pre></div>
<pre><code>## A matchit object
## - method: Optimal full matching
## - distance: Propensity score
@@ -492,9 +493,9 @@ included in the <code>matchit</code> object, even if they were not used
at all in the matching. To do so, we use the function
<code>add_s.weights()</code>, which adds sampling weights to the
supplied <code>matchit</code> objects.</p>
-<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>mF <span class="ot"><-</span> <span class="fu">add_s.weights</span>(mF, <span class="sc">~</span>SW)</span>
-<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>mF</span></code></pre></div>
+<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a>mF <span class="ot"><-</span> <span class="fu">add_s.weights</span>(mF, <span class="sc">~</span>SW)</span>
+<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a></span>
+<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a>mF</span></code></pre></div>
<pre><code>## A matchit object
## - method: Optimal full matching
## - distance: Propensity score
@@ -542,8 +543,8 @@ being supplied with the <code>s.weights</code> argument in the call to
<code>matchit()</code> or to being added afterward by
<code>add_s.weights()</code>), they will be correctly incorporated into
the balance statistics.</p>
-<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Balance before matching and for the SW propensity score full matching</span></span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mF_s)</span></code></pre></div>
+<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a><span class="co">#Balance before matching and for the SW propensity score full matching</span></span>
+<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a><span class="fu">summary</span>(mF_s)</span></code></pre></div>
<pre><code>##
## Call:
## matchit(formula = A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 +
@@ -584,8 +585,8 @@ the balance statistics.</p>
## Matched 1559. 441.
## Unmatched 0. 0.
## Discarded 0. 0.</code></pre>
-<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Balance for the non-SW propensity score full matching</span></span>
-<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mF, <span class="at">un =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a><span class="co">#Balance for the non-SW propensity score full matching</span></span>
+<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a><span class="fu">summary</span>(mF, <span class="at">un =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
<pre><code>##
## Call:
## matchit(formula = A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 +
@@ -650,24 +651,23 @@ object.</p>
<p>Below we estimate the effect of <code>A</code> on <code>Y_C</code> in
the matched and sampling weighted sample, adjusting for the covariates
to improve precision and decrease bias.</p>
-<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>md_F_s <span class="ot"><-</span> <span class="fu">match.data</span>(mF_s)</span>
-<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>fit <span class="ot"><-</span> <span class="fu">lm</span>(Y_C <span class="sc">~</span> A <span class="sc">*</span> (X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
-<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9), <span class="at">data =</span> md_F_s,</span>
-<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a> <span class="at">weights =</span> weights)</span>
-<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"marginaleffects"</span>)</span>
-<span id="cb14-8"><a href="#cb14-8" aria-hidden="true" tabindex="-1"></a><span class="fu">avg_comparisons</span>(fit,</span>
-<span id="cb14-9"><a href="#cb14-9" aria-hidden="true" tabindex="-1"></a> <span class="at">variables =</span> <span class="st">"A"</span>,</span>
-<span id="cb14-10"><a href="#cb14-10" aria-hidden="true" tabindex="-1"></a> <span class="at">vcov =</span> <span class="sc">~</span>subclass,</span>
-<span id="cb14-11"><a href="#cb14-11" aria-hidden="true" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">subset</span>(md_F_s, A <span class="sc">==</span> <span class="dv">1</span>),</span>
-<span id="cb14-12"><a href="#cb14-12" aria-hidden="true" tabindex="-1"></a> <span class="at">wts =</span> <span class="st">"weights"</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a>md_F_s <span class="ot"><-</span> <span class="fu">match.data</span>(mF_s)</span>
+<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a></span>
+<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a>fit <span class="ot"><-</span> <span class="fu">lm</span>(Y_C <span class="sc">~</span> A <span class="sc">*</span> (X1 <span class="sc">+</span> X2 <span class="sc">+</span> X3 <span class="sc">+</span> X4 <span class="sc">+</span> X5 <span class="sc">+</span> </span>
+<span id="cb14-4"><a href="#cb14-4" tabindex="-1"></a> X6 <span class="sc">+</span> X7 <span class="sc">+</span> X8 <span class="sc">+</span> X9), <span class="at">data =</span> md_F_s,</span>
+<span id="cb14-5"><a href="#cb14-5" tabindex="-1"></a> <span class="at">weights =</span> weights)</span>
+<span id="cb14-6"><a href="#cb14-6" tabindex="-1"></a></span>
+<span id="cb14-7"><a href="#cb14-7" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"marginaleffects"</span>)</span>
+<span id="cb14-8"><a href="#cb14-8" tabindex="-1"></a><span class="fu">avg_comparisons</span>(fit,</span>
+<span id="cb14-9"><a href="#cb14-9" tabindex="-1"></a> <span class="at">variables =</span> <span class="st">"A"</span>,</span>
+<span id="cb14-10"><a href="#cb14-10" tabindex="-1"></a> <span class="at">vcov =</span> <span class="sc">~</span>subclass,</span>
+<span id="cb14-11"><a href="#cb14-11" tabindex="-1"></a> <span class="at">newdata =</span> <span class="fu">subset</span>(md_F_s, A <span class="sc">==</span> <span class="dv">1</span>),</span>
+<span id="cb14-12"><a href="#cb14-12" tabindex="-1"></a> <span class="at">wts =</span> <span class="st">"weights"</span>)</span></code></pre></div>
<pre><code>##
## Term Contrast Estimate Std. Error z Pr(>|z|) 2.5 % 97.5 %
-## A 1 - 0 1.69 0.34 4.97 6.5e-07 1.03 2.36
+## A 1 - 0 1.69 0.34 4.97 <0.001 1.03 2.36
##
-## Prediction type: response
-## Columns: type, term, contrast, estimate, std.error, statistic, p.value, conf.low, conf.high</code></pre>
+## Columns: term, contrast, estimate, std.error, statistic, p.value, conf.low, conf.high</code></pre>
<p>Note that <code>match.data()</code> and <code>get_weights()</code>
have the option <code>include.s.weights</code>, which, when set to
<code>FALSE</code>, makes it so the returned weights do not incorporate
@@ -682,47 +682,47 @@ product of two.</p>
</div>
<div id="code-to-generate-data-used-in-examples" class="section level2">
<h2>Code to Generate Data used in Examples</h2>
-<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Generatng data similar to Austin (2009) for demonstrating </span></span>
-<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="co">#treatment effect estimation with sampling weights</span></span>
-<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>gen_X <span class="ot"><-</span> <span class="cf">function</span>(n) {</span>
-<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a> X <span class="ot"><-</span> <span class="fu">matrix</span>(<span class="fu">rnorm</span>(<span class="dv">9</span> <span class="sc">*</span> n), <span class="at">nrow =</span> n, <span class="at">ncol =</span> <span class="dv">9</span>)</span>
-<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a> X[,<span class="dv">5</span>] <span class="ot"><-</span> <span class="fu">as.numeric</span>(X[,<span class="dv">5</span>] <span class="sc"><</span> .<span class="dv">5</span>)</span>
-<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a> X</span>
-<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a>}</span>
-<span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a><span class="co">#~20% treated</span></span>
-<span id="cb16-10"><a href="#cb16-10" aria-hidden="true" tabindex="-1"></a>gen_A <span class="ot"><-</span> <span class="cf">function</span>(X) {</span>
-<span id="cb16-11"><a href="#cb16-11" aria-hidden="true" tabindex="-1"></a> LP_A <span class="ot"><-</span> <span class="sc">-</span> <span class="fl">1.2</span> <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">1</span>] <span class="sc">-</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">2</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">4</span>] <span class="sc">-</span> <span class="fu">log</span>(<span class="fl">2.4</span>)<span class="sc">*</span>X[,<span class="dv">5</span>] <span class="sc">+</span> </span>
-<span id="cb16-12"><a href="#cb16-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">7</span>] <span class="sc">-</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">8</span>]</span>
-<span id="cb16-13"><a href="#cb16-13" aria-hidden="true" tabindex="-1"></a> P_A <span class="ot"><-</span> <span class="fu">plogis</span>(LP_A)</span>
-<span id="cb16-14"><a href="#cb16-14" aria-hidden="true" tabindex="-1"></a> <span class="fu">rbinom</span>(<span class="fu">nrow</span>(X), <span class="dv">1</span>, P_A)</span>
-<span id="cb16-15"><a href="#cb16-15" aria-hidden="true" tabindex="-1"></a>}</span>
-<span id="cb16-16"><a href="#cb16-16" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb16-17"><a href="#cb16-17" aria-hidden="true" tabindex="-1"></a><span class="co"># Continuous outcome</span></span>
-<span id="cb16-18"><a href="#cb16-18" aria-hidden="true" tabindex="-1"></a>gen_Y_C <span class="ot"><-</span> <span class="cf">function</span>(A, X) {</span>
-<span id="cb16-19"><a href="#cb16-19" aria-hidden="true" tabindex="-1"></a> <span class="dv">2</span><span class="sc">*</span>A <span class="sc">+</span> <span class="dv">2</span><span class="sc">*</span>X[,<span class="dv">1</span>] <span class="sc">+</span> <span class="dv">2</span><span class="sc">*</span>X[,<span class="dv">2</span>] <span class="sc">+</span> <span class="dv">2</span><span class="sc">*</span>X[,<span class="dv">3</span>] <span class="sc">+</span> <span class="dv">1</span><span class="sc">*</span>X[,<span class="dv">4</span>] <span class="sc">+</span> <span class="dv">2</span><span class="sc">*</span>X[,<span class="dv">5</span>] <span class="sc">+</span> <span class="dv">1</span><span class="sc">*</span>X[,<span class="dv">6</span>] <span class="sc">+</span> <span class="fu">rnorm</span>(<span class="fu">length</span>(A), <span class="dv">0</span>, <span class="dv">5</span>)</span>
-<span id="cb16-20"><a href="#cb16-20" aria-hidden="true" tabindex="-1"></a>}</span>
-<span id="cb16-21"><a href="#cb16-21" aria-hidden="true" tabindex="-1"></a><span class="co">#Conditional:</span></span>
-<span id="cb16-22"><a href="#cb16-22" aria-hidden="true" tabindex="-1"></a><span class="co"># MD: 2</span></span>
-<span id="cb16-23"><a href="#cb16-23" aria-hidden="true" tabindex="-1"></a><span class="co">#Marginal:</span></span>
-<span id="cb16-24"><a href="#cb16-24" aria-hidden="true" tabindex="-1"></a><span class="co"># MD: 2</span></span>
-<span id="cb16-25"><a href="#cb16-25" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb16-26"><a href="#cb16-26" aria-hidden="true" tabindex="-1"></a>gen_SW <span class="ot"><-</span> <span class="cf">function</span>(X) {</span>
-<span id="cb16-27"><a href="#cb16-27" aria-hidden="true" tabindex="-1"></a> e <span class="ot"><-</span> <span class="fu">rbinom</span>(<span class="fu">nrow</span>(X), <span class="dv">1</span>, .<span class="dv">3</span>)</span>
-<span id="cb16-28"><a href="#cb16-28" aria-hidden="true" tabindex="-1"></a> <span class="dv">1</span><span class="sc">/</span><span class="fu">plogis</span>(<span class="fu">log</span>(<span class="fl">1.4</span>)<span class="sc">*</span>X[,<span class="dv">2</span>] <span class="sc">+</span> <span class="fu">log</span>(.<span class="dv">7</span>)<span class="sc">*</span>X[,<span class="dv">4</span>] <span class="sc">+</span> <span class="fu">log</span>(.<span class="dv">9</span>)<span class="sc">*</span>X[,<span class="dv">6</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">8</span>] <span class="sc">+</span> <span class="fu">log</span>(.<span class="dv">9</span>)<span class="sc">*</span>e <span class="sc">+</span></span>
-<span id="cb16-29"><a href="#cb16-29" aria-hidden="true" tabindex="-1"></a> <span class="sc">-</span><span class="fu">log</span>(.<span class="dv">5</span>)<span class="sc">*</span>e<span class="sc">*</span>X[,<span class="dv">2</span>] <span class="sc">+</span> <span class="fu">log</span>(.<span class="dv">6</span>)<span class="sc">*</span>e<span class="sc">*</span>X[,<span class="dv">4</span>])</span>
-<span id="cb16-30"><a href="#cb16-30" aria-hidden="true" tabindex="-1"></a>}</span>
-<span id="cb16-31"><a href="#cb16-31" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb16-32"><a href="#cb16-32" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">19599</span>)</span>
-<span id="cb16-33"><a href="#cb16-33" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb16-34"><a href="#cb16-34" aria-hidden="true" tabindex="-1"></a>n <span class="ot"><-</span> <span class="dv">2000</span></span>
-<span id="cb16-35"><a href="#cb16-35" aria-hidden="true" tabindex="-1"></a>X <span class="ot"><-</span> <span class="fu">gen_X</span>(n)</span>
-<span id="cb16-36"><a href="#cb16-36" aria-hidden="true" tabindex="-1"></a>A <span class="ot"><-</span> <span class="fu">gen_A</span>(X)</span>
-<span id="cb16-37"><a href="#cb16-37" aria-hidden="true" tabindex="-1"></a>SW <span class="ot"><-</span> <span class="fu">gen_SW</span>(X)</span>
-<span id="cb16-38"><a href="#cb16-38" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb16-39"><a href="#cb16-39" aria-hidden="true" tabindex="-1"></a>Y_C <span class="ot"><-</span> <span class="fu">gen_Y_C</span>(A, X)</span>
-<span id="cb16-40"><a href="#cb16-40" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb16-41"><a href="#cb16-41" aria-hidden="true" tabindex="-1"></a>d <span class="ot"><-</span> <span class="fu">data.frame</span>(A, X, Y_C, SW)</span></code></pre></div>
+<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a><span class="co">#Generatng data similar to Austin (2009) for demonstrating </span></span>
+<span id="cb16-2"><a href="#cb16-2" tabindex="-1"></a><span class="co">#treatment effect estimation with sampling weights</span></span>
+<span id="cb16-3"><a href="#cb16-3" tabindex="-1"></a>gen_X <span class="ot"><-</span> <span class="cf">function</span>(n) {</span>
+<span id="cb16-4"><a href="#cb16-4" tabindex="-1"></a> X <span class="ot"><-</span> <span class="fu">matrix</span>(<span class="fu">rnorm</span>(<span class="dv">9</span> <span class="sc">*</span> n), <span class="at">nrow =</span> n, <span class="at">ncol =</span> <span class="dv">9</span>)</span>
+<span id="cb16-5"><a href="#cb16-5" tabindex="-1"></a> X[,<span class="dv">5</span>] <span class="ot"><-</span> <span class="fu">as.numeric</span>(X[,<span class="dv">5</span>] <span class="sc"><</span> .<span class="dv">5</span>)</span>
+<span id="cb16-6"><a href="#cb16-6" tabindex="-1"></a> X</span>
+<span id="cb16-7"><a href="#cb16-7" tabindex="-1"></a>}</span>
+<span id="cb16-8"><a href="#cb16-8" tabindex="-1"></a></span>
+<span id="cb16-9"><a href="#cb16-9" tabindex="-1"></a><span class="co">#~20% treated</span></span>
+<span id="cb16-10"><a href="#cb16-10" tabindex="-1"></a>gen_A <span class="ot"><-</span> <span class="cf">function</span>(X) {</span>
+<span id="cb16-11"><a href="#cb16-11" tabindex="-1"></a> LP_A <span class="ot"><-</span> <span class="sc">-</span> <span class="fl">1.2</span> <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">1</span>] <span class="sc">-</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">2</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">4</span>] <span class="sc">-</span> <span class="fu">log</span>(<span class="fl">2.4</span>)<span class="sc">*</span>X[,<span class="dv">5</span>] <span class="sc">+</span> </span>
+<span id="cb16-12"><a href="#cb16-12" tabindex="-1"></a> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">*</span>X[,<span class="dv">7</span>] <span class="sc">-</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">8</span>]</span>
+<span id="cb16-13"><a href="#cb16-13" tabindex="-1"></a> P_A <span class="ot"><-</span> <span class="fu">plogis</span>(LP_A)</span>
+<span id="cb16-14"><a href="#cb16-14" tabindex="-1"></a> <span class="fu">rbinom</span>(<span class="fu">nrow</span>(X), <span class="dv">1</span>, P_A)</span>
+<span id="cb16-15"><a href="#cb16-15" tabindex="-1"></a>}</span>
+<span id="cb16-16"><a href="#cb16-16" tabindex="-1"></a></span>
+<span id="cb16-17"><a href="#cb16-17" tabindex="-1"></a><span class="co"># Continuous outcome</span></span>
+<span id="cb16-18"><a href="#cb16-18" tabindex="-1"></a>gen_Y_C <span class="ot"><-</span> <span class="cf">function</span>(A, X) {</span>
+<span id="cb16-19"><a href="#cb16-19" tabindex="-1"></a> <span class="dv">2</span><span class="sc">*</span>A <span class="sc">+</span> <span class="dv">2</span><span class="sc">*</span>X[,<span class="dv">1</span>] <span class="sc">+</span> <span class="dv">2</span><span class="sc">*</span>X[,<span class="dv">2</span>] <span class="sc">+</span> <span class="dv">2</span><span class="sc">*</span>X[,<span class="dv">3</span>] <span class="sc">+</span> <span class="dv">1</span><span class="sc">*</span>X[,<span class="dv">4</span>] <span class="sc">+</span> <span class="dv">2</span><span class="sc">*</span>X[,<span class="dv">5</span>] <span class="sc">+</span> <span class="dv">1</span><span class="sc">*</span>X[,<span class="dv">6</span>] <span class="sc">+</span> <span class="fu">rnorm</span>(<span class="fu">length</span>(A), <span class="dv">0</span>, <span class="dv">5</span>)</span>
+<span id="cb16-20"><a href="#cb16-20" tabindex="-1"></a>}</span>
+<span id="cb16-21"><a href="#cb16-21" tabindex="-1"></a><span class="co">#Conditional:</span></span>
+<span id="cb16-22"><a href="#cb16-22" tabindex="-1"></a><span class="co"># MD: 2</span></span>
+<span id="cb16-23"><a href="#cb16-23" tabindex="-1"></a><span class="co">#Marginal:</span></span>
+<span id="cb16-24"><a href="#cb16-24" tabindex="-1"></a><span class="co"># MD: 2</span></span>
+<span id="cb16-25"><a href="#cb16-25" tabindex="-1"></a></span>
+<span id="cb16-26"><a href="#cb16-26" tabindex="-1"></a>gen_SW <span class="ot"><-</span> <span class="cf">function</span>(X) {</span>
+<span id="cb16-27"><a href="#cb16-27" tabindex="-1"></a> e <span class="ot"><-</span> <span class="fu">rbinom</span>(<span class="fu">nrow</span>(X), <span class="dv">1</span>, .<span class="dv">3</span>)</span>
+<span id="cb16-28"><a href="#cb16-28" tabindex="-1"></a> <span class="dv">1</span><span class="sc">/</span><span class="fu">plogis</span>(<span class="fu">log</span>(<span class="fl">1.4</span>)<span class="sc">*</span>X[,<span class="dv">2</span>] <span class="sc">+</span> <span class="fu">log</span>(.<span class="dv">7</span>)<span class="sc">*</span>X[,<span class="dv">4</span>] <span class="sc">+</span> <span class="fu">log</span>(.<span class="dv">9</span>)<span class="sc">*</span>X[,<span class="dv">6</span>] <span class="sc">+</span> <span class="fu">log</span>(<span class="fl">1.5</span>)<span class="sc">*</span>X[,<span class="dv">8</span>] <span class="sc">+</span> <span class="fu">log</span>(.<span class="dv">9</span>)<span class="sc">*</span>e <span class="sc">+</span></span>
+<span id="cb16-29"><a href="#cb16-29" tabindex="-1"></a> <span class="sc">-</span><span class="fu">log</span>(.<span class="dv">5</span>)<span class="sc">*</span>e<span class="sc">*</span>X[,<span class="dv">2</span>] <span class="sc">+</span> <span class="fu">log</span>(.<span class="dv">6</span>)<span class="sc">*</span>e<span class="sc">*</span>X[,<span class="dv">4</span>])</span>
+<span id="cb16-30"><a href="#cb16-30" tabindex="-1"></a>}</span>
+<span id="cb16-31"><a href="#cb16-31" tabindex="-1"></a></span>
+<span id="cb16-32"><a href="#cb16-32" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">19599</span>)</span>
+<span id="cb16-33"><a href="#cb16-33" tabindex="-1"></a></span>
+<span id="cb16-34"><a href="#cb16-34" tabindex="-1"></a>n <span class="ot"><-</span> <span class="dv">2000</span></span>
+<span id="cb16-35"><a href="#cb16-35" tabindex="-1"></a>X <span class="ot"><-</span> <span class="fu">gen_X</span>(n)</span>
+<span id="cb16-36"><a href="#cb16-36" tabindex="-1"></a>A <span class="ot"><-</span> <span class="fu">gen_A</span>(X)</span>
+<span id="cb16-37"><a href="#cb16-37" tabindex="-1"></a>SW <span class="ot"><-</span> <span class="fu">gen_SW</span>(X)</span>
+<span id="cb16-38"><a href="#cb16-38" tabindex="-1"></a></span>
+<span id="cb16-39"><a href="#cb16-39" tabindex="-1"></a>Y_C <span class="ot"><-</span> <span class="fu">gen_Y_C</span>(A, X)</span>
+<span id="cb16-40"><a href="#cb16-40" tabindex="-1"></a></span>
+<span id="cb16-41"><a href="#cb16-41" tabindex="-1"></a>d <span class="ot"><-</span> <span class="fu">data.frame</span>(A, X, Y_C, SW)</span></code></pre></div>
</div>
<div id="references" class="section level2 unnumbered">
<h2 class="unnumbered">References</h2>
diff --git a/man/figures/README-unnamed-chunk-5-1.png b/man/figures/README-unnamed-chunk-5-1.png
index 838cb5e..492a8d2 100644
Binary files a/man/figures/README-unnamed-chunk-5-1.png and b/man/figures/README-unnamed-chunk-5-1.png differ
diff --git a/man/mahalanobis_dist.Rd b/man/mahalanobis_dist.Rd
index 99d28d9..3871069 100644
--- a/man/mahalanobis_dist.Rd
+++ b/man/mahalanobis_dist.Rd
@@ -69,7 +69,7 @@ easier without having to change the arguments supplied.}
A numeric distance matrix. When \code{formula} has a left-hand-side
(treatment) variable, the matrix will have one row for each treated unit and
one column for each control unit. Otherwise, the matrix will have one row
-and one column for each treated unit.
+and one column for each unit.
}
\description{
The functions compute a distance matrix, either for a single dataset (i.e.,
@@ -77,7 +77,7 @@ the distances between all pairs of units) or for two groups defined by a
splitting variable (i.e., the distances between all units in one group and
all units in the other). These distance matrices include the Mahalanobis
distance, Euclidean distance, scaled Euclidean distance, and robust
-(rank-based) Mahalanobs distance. These functions can be used as inputs to
+(rank-based) Mahalanobis distance. These functions can be used as inputs to
the \code{distance} argument to \code{\link[=matchit]{matchit()}} and are used to compute the
corresponding distance matrices within \code{matchit()} when named.
}
diff --git a/man/match.data.Rd b/man/match.data.Rd
index 5afdb43..e9bfd34 100644
--- a/man/match.data.Rd
+++ b/man/match.data.Rd
@@ -72,8 +72,8 @@ Only used with \code{get_matches()}; for \code{match.data()}, the units IDs
are stored in the row names of the returned data frame.}
}
\value{
-A data frame containing the data supplied in the \code{data}
-argument or in the original call to \code{matchit()} with the computed
+A data frame containing the data supplied in the \code{data} argument or in the
+original call to \code{matchit()} with the computed
output variables appended as additional columns, named according the
arguments above. For \code{match.data()}, the \code{group} and
\code{drop.unmatched} arguments control whether only subsets of the data are
@@ -130,7 +130,7 @@ subset of matched units. The idea is for the output of \code{match.data()}
to be used as the dataset input in calls to \code{glm()} or similar to
estimate treatment effects in the matched sample. It is important to include
the weights in the estimation of the effect and its standard error. The
-subclass column, when created, contains par or subclass membership and
+subclass column, when created, contains pair or subclass membership and
should be used to estimate the effect and its standard error. Subclasses
will only be included if there is a \code{subclass} component in the
\code{matchit} object, which does not occur with matching with replacement,
@@ -145,8 +145,8 @@ units do not belong to a single matched pair. In this case, the output of
each pair they are a part of. For example, if matching was performed with
replacement and a control unit was matched to two treated units, that
control unit will have two rows in the output dataset, one for each pair it
-is a part of. Weights are computed for each row, and are equal to the
-inverse of the number of control units in each control unit's subclass.
+is a part of. Weights are computed for each row, and, for control units, are equal to the
+inverse of the number of control units in each control unit's subclass; treated units get a weight of 1.
Unmatched units are dropped. An additional column with unit IDs will be
created (named using the \code{id} argument) to identify when the same unit
is present in multiple rows. This dataset structure allows for the inclusion
diff --git a/man/matchit.Rd b/man/matchit.Rd
index 5840503..3579b73 100644
--- a/man/matchit.Rd
+++ b/man/matchit.Rd
@@ -141,15 +141,7 @@ individual methods pages for information on whether and how this argument is
used. Default is \code{FALSE} for matching without replacement.}
\item{m.order}{for methods that allow it, the order that the matching takes
-place. Allowable options depend on the matching method but include
-\code{"largest"}, where matching takes place in descending order of distance
-measures; \code{"smallest"}, where matching takes place in ascending order
-of distance measures; \code{"random"}, where matching takes place in a
-random order; and \code{"data"} where matching takes place based on the
-order of units in the data. When \code{m.order = "random"}, results may
-differ across different runs of the same code unless a seed is set and
-specified with \code{\link[=set.seed]{set.seed()}}. See the individual methods pages for
-information on whether and how this argument is used. The default of
+place. Allowable options depend on the matching method. The default of
\code{NULL} corresponds to \code{"largest"} when a propensity score is
estimated or supplied as a vector and \code{"data"} otherwise.}
@@ -334,14 +326,14 @@ unit is assigned to a subclass, which represents the pair they are a part of
(in the case of k:1 matching) or the stratum they belong to (in the case of
exact matching, coarsened exact matching, full matching, or
subclassification). The formula for computing the weights depends on the
-argument supplied to \code{estimand}. A new stratum "propensity score"
-(\code{p}) is computed as the proportion of units in each stratum that are
+argument supplied to \code{estimand}. A new "stratum propensity score"
+(\code{sp}) is computed as the proportion of units in each stratum that are
in the treated group, and all units in that stratum are assigned that
-propensity score. Weights are then computed using the standard formulas for
-inverse probability weights: for the ATT, weights are 1 for the treated
-units and \code{p/(1-p)} for the control units; for the ATC, weights are
-\code{(1-p)/p} for the treated units and 1 for the control units; for the
-ATE, weights are \code{1/p} for the treated units and \code{1/(1-p)} for the
+stratum propensity score. This is distinct from the propensity score used for matching, if any. Weights are then computed using the standard formulas for
+inverse probability weights with the stratum propensity score inserted: for the ATT, weights are 1 for the treated
+units and \code{sp/(1-sp)} for the control units; for the ATC, weights are
+\code{(1-sp)/sp} for the treated units and 1 for the control units; for the
+ATE, weights are \code{1/sp} for the treated units and \code{1/(1-sp)} for the
control units. For cardinality matching, all matched units receive a weight
of 1.
diff --git a/man/method_cem.Rd b/man/method_cem.Rd
index 6058ba5..aa337d1 100644
--- a/man/method_cem.Rd
+++ b/man/method_cem.Rd
@@ -125,6 +125,7 @@ and \code{cutpoints} is used for binning numeric covariates. The values
supplied to these arguments should be iteratively changed until a matching
solution that balances covariate balance and remaining sample size is
obtained. The arguments are described below.
+\subsection{\code{grouping}}{
The argument to \code{grouping} must be a list, where each component has the
name of a categorical variable, the levels of which are to be combined. Each
@@ -139,7 +140,10 @@ each variable will be left alone (so \code{c("none")} could have been
omitted from the previous code). Note that if a categorical variable does
not appear in \code{grouping}, it will not be coarsened, so exact matching
will take place on it. \code{grouping} should not be used for numeric
-variables; use \code{cutpoints}, described below, instead.
+variables with more than a few values; use \code{cutpoints}, described below, instead.
+}
+
+\subsection{\code{cutpoints}}{
The argument to \code{cutpoints} must also be a list, where each component
has the name of a numeric variables that is to be binned. (As a shortcut, it
@@ -148,7 +152,7 @@ Each component can take one of three forms: a vector of cutpoints that
separate the bins, a single number giving the number of bins, or a string
corresponding to an algorithm used to compute the number of bins. Any values
at a boundary will be placed into the higher bin; e.g., if the cutpoints
-were \code{(c(0, 5, 10))}, values of 5 would be placed into the same bin as
+were \code{c(0, 5, 10)}, values of 5 would be placed into the same bin as
values of 6, 7, 8, or 9, and values of 10 would be placed into a different
bin. Internally, values of \code{-Inf} and \code{Inf} are appended to the
beginning and end of the range. When given as a single number defining the
@@ -175,6 +179,8 @@ into bins based on the provided boundaries, \code{X3} into a number of bins
determined by \code{\link[grDevices:nclass]{grDevices::nclass.scott()}}, and \code{X4} into
quintiles. All other numeric variables would be split into a number of bins
determined by \code{\link[grDevices:nclass]{grDevices::nclass.Sturges()}}, the default.
+}
+
}
}
\note{
diff --git a/man/method_genetic.Rd b/man/method_genetic.Rd
index 226b16a..23aed56 100644
--- a/man/method_genetic.Rd
+++ b/man/method_genetic.Rd
@@ -68,9 +68,16 @@ process of matching.}
\item{replace}{whether matching should be done with replacement.}
-\item{m.order}{the order that the matching takes place. The default is
-\code{"largest"} when \code{distance} corresponds to a propensity score and
-\code{"data"} otherwise. See \code{\link[=matchit]{matchit()}} for allowable options.}
+\item{m.order}{the order that the matching takes place. Allowable options
+include \code{"largest"}, where matching takes place in descending order of
+distance measures; \code{"smallest"}, where matching takes place in ascending
+order of distance measures; \code{"random"}, where matching takes place
+in a random order; and \code{"data"} where matching takes place based on the
+order of units in the data. When \code{m.order = "random"}, results may differ
+across different runs of the same code unless a seed is set and specified
+with \code{\link[=set.seed]{set.seed()}}. The default of \code{NULL} corresponds to \code{"largest"} when a
+propensity score is estimated or supplied as a vector and \code{"data"}
+otherwise.}
\item{caliper}{the width(s) of the caliper(s) used for caliper matching. See
Details and Examples.}
diff --git a/man/method_nearest.Rd b/man/method_nearest.Rd
index b0283da..b211d9b 100644
--- a/man/method_nearest.Rd
+++ b/man/method_nearest.Rd
@@ -49,9 +49,17 @@ into propensity score models and balance statistics.}
\item{replace}{whether matching should be done with replacement.}
-\item{m.order}{the order that the matching takes place. The default is
-\code{"largest"} when \code{distance} corresponds to a propensity score and
-\code{"data"} otherwise. See \code{\link[=matchit]{matchit()}} for allowable options.}
+\item{m.order}{the order that the matching takes place. Allowable options
+include \code{"largest"}, where matching takes place in descending order of
+distance measures; \code{"smallest"}, where matching takes place in ascending
+order of distance measures; \code{"closest"}, where matching takes place in
+order of the distance between units; \code{"random"}, where matching takes place
+in a random order; and \code{"data"} where matching takes place based on the
+order of units in the data. When \code{m.order = "random"}, results may differ
+across different runs of the same code unless a seed is set and specified
+with \code{\link[=set.seed]{set.seed()}}. The default of \code{NULL} corresponds to \code{"largest"} when a
+propensity score is estimated or supplied as a vector and \code{"data"}
+otherwise.}
\item{caliper}{the width(s) of the caliper(s) used for caliper matching. See
Details and Examples.}
@@ -220,6 +228,11 @@ specified, it is set to 1 by default. \code{min.controls} must be less than
\code{ratio}, and \code{max.controls} must be greater than \code{ratio}. See
Examples below for an example of their use.
}
+
+\subsection{Using \code{m.order = "closest"}}{
+
+As of version 4.6.0, \code{m.order} can be set to \code{"closest"}, which works regardless of how the distance measure is specified. This matches in order of the distance between units. The closest pair of units across all potential pairs of units will be matched first; the second closest pair of all potential pairs will be matched second, etc. This ensures that the best possible matches are given priority, and in that sense performs similarly to \code{m.order = "smallest"}.
+}
}
\note{
Sometimes an error will be produced by \emph{Rcpp} along the lines of
diff --git a/man/method_quick.Rd b/man/method_quick.Rd
index 95edcdc..b466ba1 100644
--- a/man/method_quick.Rd
+++ b/man/method_quick.Rd
@@ -66,7 +66,7 @@ In \code{\link[=matchit]{matchit()}}, setting \code{method = "quick"} performs g
matching, which is a form of subclassification wherein all units, both
treatment and control (i.e., the "full" sample), are assigned to a subclass
and receive at least one match. It uses an algorithm that is extremely fast
-compared to optimal full matching, which is why it is labelled as "quick", at the
+compared to optimal full matching, which is why it is labeled as "quick", at the
expense of true optimality. The method is described in Sävje, Higgins, & Sekhon (2021). The method relies on and is a wrapper
for \pkgfun{quickmatch}{quickmatch}.
diff --git a/man/plot.matchit.Rd b/man/plot.matchit.Rd
index 902fd6e..9b1dd43 100644
--- a/man/plot.matchit.Rd
+++ b/man/plot.matchit.Rd
@@ -69,7 +69,7 @@ Major deviations indicate departures from distributional balance. With
variable with fewer than 5 unique values, points are jittered to more easily
visualize counts.
-With \code{type = "ecdf"}, empirical cumulative density function (eCDF)
+With \code{type = "ecdf"}, empirical cumulative distribution function (eCDF)
plots are created for each covariate before and after matching. Two eCDF
lines are produced in each plot: a gray one for control units and a black
one for treated units. Each point on the lines corresponds to the proportion
diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp
index 1478349..4adbd0b 100644
--- a/src/RcppExports.cpp
+++ b/src/RcppExports.cpp
@@ -49,6 +49,28 @@ BEGIN_RCPP
return rcpp_result_gen;
END_RCPP
}
+// nn_matchC_closest
+IntegerMatrix nn_matchC_closest(const NumericMatrix& distance_mat, const IntegerVector& treat, const IntegerVector& ratio, const LogicalVector& discarded, const int& reuse_max, const Nullable<IntegerMatrix>& exact_, const Nullable<double>& caliper_dist_, const Nullable<NumericVector>& caliper_covs_, const Nullable<NumericMatrix>& caliper_covs_mat_, const Nullable<IntegerMatrix>& antiexact_covs_, const Nullable<IntegerVector>& unit_id_, const bool& disl_prog);
+RcppExport SEXP _MatchIt_nn_matchC_closest(SEXP distance_matSEXP, SEXP treatSEXP, SEXP ratioSEXP, SEXP discardedSEXP, SEXP reuse_maxSEXP, SEXP exact_SEXP, SEXP caliper_dist_SEXP, SEXP caliper_covs_SEXP, SEXP caliper_covs_mat_SEXP, SEXP antiexact_covs_SEXP, SEXP unit_id_SEXP, SEXP disl_progSEXP) {
+BEGIN_RCPP
+ Rcpp::RObject rcpp_result_gen;
+ Rcpp::RNGScope rcpp_rngScope_gen;
+ Rcpp::traits::input_parameter< const NumericMatrix& >::type distance_mat(distance_matSEXP);
+ Rcpp::traits::input_parameter< const IntegerVector& >::type treat(treatSEXP);
+ Rcpp::traits::input_parameter< const IntegerVector& >::type ratio(ratioSEXP);
+ Rcpp::traits::input_parameter< const LogicalVector& >::type discarded(discardedSEXP);
+ Rcpp::traits::input_parameter< const int& >::type reuse_max(reuse_maxSEXP);
+ Rcpp::traits::input_parameter< const Nullable<IntegerMatrix>& >::type exact_(exact_SEXP);
+ Rcpp::traits::input_parameter< const Nullable<double>& >::type caliper_dist_(caliper_dist_SEXP);
+ Rcpp::traits::input_parameter< const Nullable<NumericVector>& >::type caliper_covs_(caliper_covs_SEXP);
+ Rcpp::traits::input_parameter< const Nullable<NumericMatrix>& >::type caliper_covs_mat_(caliper_covs_mat_SEXP);
+ Rcpp::traits::input_parameter< const Nullable<IntegerMatrix>& >::type antiexact_covs_(antiexact_covs_SEXP);
+ Rcpp::traits::input_parameter< const Nullable<IntegerVector>& >::type unit_id_(unit_id_SEXP);
+ Rcpp::traits::input_parameter< const bool& >::type disl_prog(disl_progSEXP);
+ rcpp_result_gen = Rcpp::wrap(nn_matchC_closest(distance_mat, treat, ratio, discarded, reuse_max, exact_, caliper_dist_, caliper_covs_, caliper_covs_mat_, antiexact_covs_, unit_id_, disl_prog));
+ return rcpp_result_gen;
+END_RCPP
+}
// nn_matchC_vec
IntegerMatrix nn_matchC_vec(const IntegerVector& treat_, const IntegerVector& ord_, const IntegerVector& ratio_, const LogicalVector& discarded_, const int& reuse_max, const NumericVector& distance_, const Nullable<IntegerMatrix>& exact_, const Nullable<double>& caliper_dist_, const Nullable<NumericVector>& caliper_covs_, const Nullable<NumericMatrix>& caliper_covs_mat_, const Nullable<IntegerMatrix>& antiexact_covs_, const Nullable<IntegerVector>& unit_id_, const bool& disl_prog);
RcppExport SEXP _MatchIt_nn_matchC_vec(SEXP treat_SEXP, SEXP ord_SEXP, SEXP ratio_SEXP, SEXP discarded_SEXP, SEXP reuse_maxSEXP, SEXP distance_SEXP, SEXP exact_SEXP, SEXP caliper_dist_SEXP, SEXP caliper_covs_SEXP, SEXP caliper_covs_mat_SEXP, SEXP antiexact_covs_SEXP, SEXP unit_id_SEXP, SEXP disl_progSEXP) {
@@ -141,6 +163,7 @@ RcppExport SEXP _MatchIt_RcppExport_registerCCallable() {
static const R_CallMethodDef CallEntries[] = {
{"_MatchIt_dist_to_matrixC", (DL_FUNC) &_MatchIt_dist_to_matrixC, 1},
{"_MatchIt_nn_matchC", (DL_FUNC) &_MatchIt_nn_matchC, 15},
+ {"_MatchIt_nn_matchC_closest", (DL_FUNC) &_MatchIt_nn_matchC_closest, 12},
{"_MatchIt_nn_matchC_vec", (DL_FUNC) &_MatchIt_nn_matchC_vec, 13},
{"_MatchIt_pairdistsubC", (DL_FUNC) &_MatchIt_pairdistsubC, 4},
{"_MatchIt_subclass2mmC", (DL_FUNC) &_MatchIt_subclass2mmC, 3},
diff --git a/src/nn_matchC.cpp b/src/nn_matchC.cpp
index 8e7848d..ccb0dac 100644
--- a/src/nn_matchC.cpp
+++ b/src/nn_matchC.cpp
@@ -53,7 +53,7 @@ IntegerMatrix nn_matchC(const IntegerVector& treat_,
int max_rat = max(ratio);
IntegerMatrix mm(n1_, max_rat);
mm.fill(NA_INTEGER);
- rownames(mm) = lab[ind1_];
+ // rownames(mm) = lab[ind1_];
// Store who has been matched
IntegerVector matched = rep(0, n_);
@@ -172,7 +172,7 @@ IntegerMatrix nn_matchC(const IntegerVector& treat_,
//Prevent control units being matched to same treated unit again
if (rat > 0) {
- c_eligible = as<IntegerVector>(c_eligible[!in(c_eligible, na_omit(mm.row(t)) - 1)]);
+ c_eligible = as<IntegerVector>(c_eligible[!in(c_eligible, na_omit(mm.row(t)))]);
}
if (use_exact) {
@@ -185,7 +185,7 @@ IntegerMatrix nn_matchC(const IntegerVector& treat_,
}
if (use_antiexact) {
- for (a = 0; a < n_anti && c_eligible.size() > 0; ++a) {
+ for (a = 0; (a < n_anti) && (c_eligible.size() > 0); ++a) {
antiexact_col = antiexact_covs(_, a);
antiexact_t = antiexact_col[t_ind];
antiexact_col = antiexact_col[c_eligible];
@@ -218,7 +218,7 @@ IntegerMatrix nn_matchC(const IntegerVector& treat_,
}
if (use_caliper_covs) {
- for (x = 0; (x < cal_len) && c_eligible.size() > 0; ++x) {
+ for (x = 0; (x < cal_len) && (c_eligible.size() > 0); ++x) {
cal_var = caliper_covs_mat( _ , x );
cal_var_t = cal_var[t_ind];
@@ -241,7 +241,7 @@ IntegerMatrix nn_matchC(const IntegerVector& treat_,
//If replace and few eligible controls, assign all and move on
if (!use_reuse_max && (num_eligible <= t_rat)) {
for (j = 0; j < num_eligible; ++j) {
- mm( t , j ) = c_eligible[j] + 1;
+ mm( t , j ) = c_eligible[j];
}
continue;
}
@@ -285,14 +285,14 @@ IntegerMatrix nn_matchC(const IntegerVector& treat_,
for (j = 0; j < t_rat; ++j) {
min_ind = indices[j];
- mm( t , j ) = c_eligible[min_ind] + 1;
+ mm( t , j ) = c_eligible[min_ind];
}
}
else {
min_ind = which_min(match_distance);
c_chosen = c_eligible[min_ind];
- mm( t , rat ) = c_chosen + 1; // + 1 because C indexing starts at 0 but mm is sent to R
+ mm( t , rat ) = c_chosen;
id_of_chosen_unit = unit_id[c_chosen];
units_with_id_of_chosen_unit = ind_[unit_id == id_of_chosen_unit];
@@ -307,5 +307,8 @@ IntegerMatrix nn_matchC(const IntegerVector& treat_,
p.update(prog_length);
+ mm = mm + 1; // + 1 because C indexing starts at 0 but mm is sent to R
+ rownames(mm) = lab[ind1_];
+
return mm;
}
diff --git a/src/nn_matchC_closest.cpp b/src/nn_matchC_closest.cpp
new file mode 100644
index 0000000..22ce728
--- /dev/null
+++ b/src/nn_matchC_closest.cpp
@@ -0,0 +1,197 @@
+// [[Rcpp::depends(RcppProgress)]]
+#include <progress.hpp>
+#include <Rcpp.h>
+#include "internal.h"
+using namespace Rcpp;
+
+// [[Rcpp::plugins(cpp11)]]
+
+// [[Rcpp::export]]
+IntegerMatrix nn_matchC_closest(const NumericMatrix& distance_mat,
+ const IntegerVector& treat,
+ const IntegerVector& ratio,
+ const LogicalVector& discarded,
+ const int& reuse_max,
+ const Nullable<IntegerMatrix>& exact_ = R_NilValue,
+ const Nullable<double>& caliper_dist_ = R_NilValue,
+ const Nullable<NumericVector>& caliper_covs_ = R_NilValue,
+ const Nullable<NumericMatrix>& caliper_covs_mat_ = R_NilValue,
+ const Nullable<IntegerMatrix>& antiexact_covs_ = R_NilValue,
+ const Nullable<IntegerVector>& unit_id_ = R_NilValue,
+ const bool& disl_prog = false)
+{
+
+ int r = distance_mat.nrow();
+ int c = distance_mat.ncol();
+
+ IntegerMatrix mm(r, max(ratio));
+ mm.fill(NA_INTEGER);
+
+ CharacterVector lab = treat.names();
+
+ IntegerVector matched_t = rep(0, r);
+ IntegerVector matched_c = rep(0, c);
+
+ // IntegerVector ind = seq(0, treat.size() - 1);
+ IntegerVector ind0 = which(treat == 0);
+ IntegerVector ind1 = which(treat == 1);
+
+ //caliper_dist
+ bool use_caliper_dist = false;
+ double caliper_dist;
+ if (caliper_dist_.isNotNull()) {
+ caliper_dist = as<double>(caliper_dist_);
+ use_caliper_dist = true;
+ }
+
+ //caliper_covs
+ NumericVector caliper_covs;
+ NumericMatrix caliper_covs_mat;
+ bool use_caliper_covs = false;
+ double n_cal_covs;
+ if (caliper_covs_.isNotNull()) {
+ caliper_covs = as<NumericVector>(caliper_covs_);
+ caliper_covs_mat = as<NumericMatrix>(caliper_covs_mat_);
+ n_cal_covs = caliper_covs_mat.ncol();
+ use_caliper_covs = true;
+ }
+
+ //exact
+ bool use_exact = false;
+ IntegerVector exact;
+ if (exact_.isNotNull()) {
+ exact = as<IntegerVector>(exact_);
+ use_exact = true;
+ }
+
+ //antiexact
+ IntegerMatrix antiexact_covs;
+ bool use_antiexact = false;
+ int n_anti;
+ if (antiexact_covs_.isNotNull()) {
+ antiexact_covs = as<IntegerMatrix>(antiexact_covs_);
+ n_anti = antiexact_covs.ncol();
+ use_antiexact = true;
+ }
+
+ //unit_id
+ IntegerVector unit_id, ck_;
+ bool use_unit_id = false;
+ if (unit_id_.isNotNull()) {
+ unit_id = as<IntegerVector>(unit_id_);
+ use_unit_id = true;
+ }
+
+ //progress bar
+ int prog_length;
+ prog_length = sum(ratio) + 1;
+ Progress p(prog_length, disl_prog);
+ p.increment();
+
+ Function o("order");
+
+ IntegerVector d_ord = o(distance_mat);
+ d_ord = d_ord - 1; //Because R uses 1-indexing
+
+ int rj, cj, dj, i, ind0i, ind1i;
+ bool okay;
+
+ for (int j = 0; j < d_ord.size(); j++) {
+
+ dj = d_ord[j];
+
+ // If distance is greater tha distance caliper, stop the whole thing because
+ // no remaining distance will be smaller
+ if (use_caliper_dist) {
+ if (distance_mat[dj] > caliper_dist) break;
+ }
+
+ // Get row and column index of potential pair
+ rj = dj % r;
+ cj = dj / r;
+
+ // Get sample indices of members of potential pair
+ ind1i = ind1[rj];
+ ind0i = ind0[cj];
+
+ // If either member is discarded, move on
+ if (discarded[ind1i]) continue;
+ if (discarded[ind0i]) continue;
+
+ // If either member has been matched enough times, move on
+ if (matched_t[rj] >= ratio[rj]) continue;
+ if (matched_c[cj] >= reuse_max) continue;
+
+ // Exact matching criterion
+ if (use_exact) {
+ if (exact[ind1i] != exact[ind0i]) {
+ continue;
+ }
+ }
+
+ // Covariate caliper criterion
+ if (use_caliper_covs) {
+ i = 0;
+ okay = true;
+ while (okay && (i < n_cal_covs)) {
+ if (std::abs(caliper_covs_mat(ind1i, i) - caliper_covs_mat(ind0i, i)) > caliper_covs[i]) {
+ okay = false;
+ }
+ i++;
+ }
+ if (!okay) continue;
+ }
+
+ // Antiexact criterion
+ if (use_antiexact) {
+ i = 0;
+ okay = true;
+ while (okay && (i < n_anti)) {
+ if (antiexact_covs(ind1i, i) == antiexact_covs(ind0i, i)) {
+ okay = false;
+ }
+ i++;
+ }
+ if (!okay) continue;
+ }
+
+ // If all criteria above are satisfied, potential pair becomes a pair!
+
+ // If unit_id used, increase match count of all units with that ID
+ if (use_unit_id) {
+ ck_ = which(as<IntegerVector>(unit_id[ind1]) == unit_id[ind1i]);
+
+ for (i = 0; i < ck_.size(); i++) {
+ matched_t[ck_[i]]++;
+ }
+
+ ck_ = which(as<IntegerVector>(unit_id[ind0]) == unit_id[ind0i]);
+
+ for (i = 0; i < ck_.size(); i++) {
+ matched_c[ck_[i]]++;
+ }
+ }
+ else {
+ matched_t[rj]++;
+ matched_c[cj]++;
+ }
+
+ mm(rj, matched_t[rj] - 1) = ind0i;
+
+ p.increment();
+
+ if (matched_t[rj] >= ratio[rj]) {
+ if (all(matched_t >= ratio).is_true()) break;
+ }
+ if (matched_c[cj] >= reuse_max) {
+ if (all(matched_c >= reuse_max).is_true()) break;
+ }
+ }
+
+ p.update(prog_length);
+
+ mm = mm + 1;
+ rownames(mm) = lab[treat == 1];
+
+ return mm;
+}
diff --git a/src/nn_matchC_vec.cpp b/src/nn_matchC_vec.cpp
index bec4c23..43c0409 100644
--- a/src/nn_matchC_vec.cpp
+++ b/src/nn_matchC_vec.cpp
@@ -9,7 +9,10 @@ using namespace Rcpp;
bool check_in(int x,
IntegerVector table) {
- for (int j = 0; j < table.size(); j++) {
+ int t = table.size();
+ if (t < 1) return false;
+
+ for (int j = 0; j < t; j++) {
if (x == table[j]) return true;
}
return false;
@@ -49,6 +52,11 @@ int find_right(int ii,
continue;
}
+ if (!can_be_matched[k]) {
+ k++; //if unit is matched, move right
+ continue;
+ }
+
//if unit has already been matched to unit i, skip
if (r > 0) {
if (check_in(d_ord[k], row)) {
@@ -57,13 +65,8 @@ int find_right(int ii,
}
}
- if (!can_be_matched[k]) {
- k++; //if unit is matched, move right
- continue;
- }
-
if (use_caliper_dist) {
- if (abs(distance[ii] - distance[k]) > caliper_dist) {
+ if (std::abs(distance[ii] - distance[k]) > caliper_dist) {
//if closest is outside caliper, break; none can be found
break;
}
@@ -95,7 +98,7 @@ int find_right(int ii,
i = 0;
okay = true;
while (okay && (i < n_cal_covs)) {
- if (abs(caliper_covs_mat(ii, i) - caliper_covs_mat(k, i)) > caliper_covs[i]) {
+ if (std::abs(caliper_covs_mat(ii, i) - caliper_covs_mat(k, i)) > caliper_covs[i]) {
okay = false;
}
i++;
@@ -148,6 +151,11 @@ int find_left(int ii,
continue;
}
+ if (!can_be_matched[k]) {
+ k--; //if unit is matched, move left
+ continue;
+ }
+
//if unit has already been matched to unit i, skip
if (r > 0) {
if (check_in(d_ord[k], row)) {
@@ -156,13 +164,8 @@ int find_left(int ii,
}
}
- if (!can_be_matched[k]) {
- k--; //if unit is matched, move left
- continue;
- }
-
if (use_caliper_dist) {
- if (abs(distance[ii] - distance[k]) > caliper_dist) {
+ if (std::abs(distance[ii] - distance[k]) > caliper_dist) {
//if closest is outside caliper, break
break;
}
@@ -194,7 +197,7 @@ int find_left(int ii,
i = 0;
okay = true;
while (okay && (i < n_cal_covs)) {
- if (abs(caliper_covs_mat(ii, i) - caliper_covs_mat(k, i)) > caliper_covs[i]) {
+ if (std::abs(caliper_covs_mat(ii, i) - caliper_covs_mat(k, i)) > caliper_covs[i]) {
okay = false;
}
i++;
@@ -213,6 +216,8 @@ int find_left(int ii,
return k;
}
+// [[Rcpp::plugins(cpp11)]]
+
// [[Rcpp::export]]
IntegerMatrix nn_matchC_vec(const IntegerVector& treat_,
const IntegerVector& ord_,
@@ -232,8 +237,8 @@ IntegerMatrix nn_matchC_vec(const IntegerVector& treat_,
CharacterVector lab_ = treat_.names();
- //Use base::sort.list() because faster than Rcpp implementation of order()
- Function o("sort.list");
+ //Use base::order() because faster than Rcpp implementation of order()
+ Function o("order");
IntegerVector d_ord = o(distance_, Named("decreasing") = false);
d_ord = d_ord - 1;
@@ -247,10 +252,6 @@ IntegerMatrix nn_matchC_vec(const IntegerVector& treat_,
ratio_tmp[treat_ == 1] = ratio_;
IntegerVector ratio = ratio_tmp[d_ord];
- // IntegerVector ord_tmp(n);
- // ord_tmp[treat_ == 1] = ord_;
- // IntegerVector ord = ord_tmp[d_ord];
- // ord = ord - 1;
IntegerVector ord = ord_ - 1;
int max_ratio = max(ratio);
@@ -273,7 +274,7 @@ IntegerMatrix nn_matchC_vec(const IntegerVector& treat_,
mm.fill(NA_INTEGER);
CharacterVector lab1 = lab[ind1];
CharacterVector mm_nm = lab_[treat_ == 1];
- rownames(mm) = mm_nm;
+
//caliper_dist
double caliper_dist;
@@ -330,7 +331,7 @@ IntegerMatrix nn_matchC_vec(const IntegerVector& treat_,
}
IntegerVector times_matched = rep(0, n);
- LogicalVector can_be_matched = (!as<LogicalVector>(discarded)) & (treat != 1);
+ LogicalVector can_be_matched = !as<LogicalVector>(discarded);
IntegerVector ind_cbm = ind[can_be_matched];
int first_control = ind_cbm[0];
@@ -346,6 +347,7 @@ IntegerMatrix nn_matchC_vec(const IntegerVector& treat_,
double dti;
String labi;
IntegerVector mm_row, ck_;
+ bool done = false;
for (r = 0; r < max_ratio; r++) {
for (i = 0; i < n1; i++) {
@@ -357,9 +359,7 @@ IntegerMatrix nn_matchC_vec(const IntegerVector& treat_,
ii = ind1[labi]; //ii'th unit overall
- if (discarded[ii]) continue;
-
- if (ratio[ii] < r + 1) continue;
+ if (!can_be_matched[ii]) continue;
mm_row = na_omit(mm(row_to_fill, _));
@@ -386,7 +386,7 @@ IntegerMatrix nn_matchC_vec(const IntegerVector& treat_,
if ((k_left >= 0) && (k_right >= 0)) {
dti = distance[ii];
- if (abs(distance[k_left] - dti) <= abs(distance[k_right] - dti)) {
+ if (std::abs(distance[k_left] - dti) <= std::abs(distance[k_right] - dti)) {
k = k_left;
}
else {
@@ -400,13 +400,23 @@ IntegerMatrix nn_matchC_vec(const IntegerVector& treat_,
k = k_right;
}
else {
+ can_be_matched[ii] = false;
continue;
}
-
- mm( row_to_fill, r ) = d_ord[k] + 1;
+ mm( row_to_fill, r ) = d_ord[k];
if (use_unit_id) {
+ ck_ = ind[unit_id == unit_id[ii]];
+
+ for (j = 0; j < ck_.size(); j++) {
+ ck = ck_[j];
+ times_matched[ck]++;
+ if (times_matched[ck] >= ratio[ck]) {
+ can_be_matched[ck] = false;
+ }
+ }
+
ck_ = ind[unit_id == unit_id[k]];
for (j = 0; j < ck_.size(); j++) {
@@ -418,18 +428,32 @@ IntegerMatrix nn_matchC_vec(const IntegerVector& treat_,
}
}
else {
+ times_matched[ii]++;
+ if (times_matched[ii] >= ratio[ii]) {
+ can_be_matched[ii] = false;
+ }
+
times_matched[k]++;
if (times_matched[k] >= reuse_max) {
can_be_matched[k] = false;
}
}
- if (any(can_be_matched).is_false()) {
- p.update(prog_length);
- return mm;
+ if (!can_be_matched[ii]) {
+ if (any(as<LogicalVector>(can_be_matched[treat == 1])).is_false()) {
+ done = true;
+ break;
+ }
+ }
+
+ if (!can_be_matched[k]) {
+ if (any(as<LogicalVector>(can_be_matched[treat == 0])).is_false()) {
+ done = true;
+ break;
+ }
}
- ind_cbm = ind[can_be_matched];
+ ind_cbm = ind[can_be_matched & (treat == 0)];
if (!can_be_matched[first_control]) {
first_control = ind_cbm[0];
}
@@ -437,9 +461,14 @@ IntegerMatrix nn_matchC_vec(const IntegerVector& treat_,
last_control = ind_cbm[ind_cbm.size() - 1];
}
}
+
+ if (done) break;
}
p.update(prog_length);
+ mm = mm + 1;
+ rownames(mm) = mm_nm;
+
return mm;
}
diff --git a/vignettes/MatchIt.Rmd b/vignettes/MatchIt.Rmd
index fa17963..eff196b 100644
--- a/vignettes/MatchIt.Rmd
+++ b/vignettes/MatchIt.Rmd
@@ -97,7 +97,7 @@ Below we assess balance on the unmatched data using `summary()`:
summary(m.out0)
```
-We can see severe imbalances as measured by the standardized mean differences (`Std. Mean Diff.`), variance ratios (`Var. Ratio`), and empirical cumulative density function (eCDF) statistics. Values of standardized mean differences and eCDF statistics close to zero and values of variance ratios close to one indicate good balance, and here many of them are far from their ideal values.
+We can see severe imbalances as measured by the standardized mean differences (`Std. Mean Diff.`), variance ratios (`Var. Ratio`), and empirical cumulative distribution function (eCDF) statistics. Values of standardized mean differences and eCDF statistics close to zero and values of variance ratios close to one indicate good balance, and here many of them are far from their ideal values.
## Matching
@@ -240,7 +240,7 @@ To report matching results in a manuscript or research report, a few key pieces
> We used propensity score matching to estimate the average marginal effect of the treatment on 1978 earnings on those who received it accounting for confounding by the included covariates. We first attempted 1:1 nearest neighbor propensity score matching without replacement with a propensity score estimated using logistic regression of the treatment on the covariates. This matching specification yielded poor balance, so we instead tried full matching on the propensity score, which yielded adequate balance, as indicated in Table 1 and Figure 1. The propensity score was estimated using a probit regression of the treatment on the covariates, which yielded better balance than did a logistic regression. After matching, all standardized mean differences for the covariates were below 0.1 and all standardized mean differences for squares and two-way interactions between covariates were below .15, indicating adequate balance. Full matching uses all treated and all control units, so no units were discarded by the matching.
>
-> To estimate the treatment effect and its standard error, we fit a linear regression model with 1978 earnings as the outcome and the treatment, covariates, and their interaction as predictors and included the full matching weights in the estimation. The `lm()` function was used to fit the outcome, and the `comparisons()` function in the `marginaleffects` package was used to perform g-comptuation in the matched sample to estimate the ATT. A cluster-robust variance was used to estimate its standard error with matching stratum membership as the clustering variable.
+> To estimate the treatment effect and its standard error, we fit a linear regression model with 1978 earnings as the outcome and the treatment, covariates, and their interaction as predictors and included the full matching weights in the estimation. The `lm()` function was used to fit the outcome, and the `comparisons()` function in the `marginaleffects` package was used to perform g-computation in the matched sample to estimate the ATT. A cluster-robust variance was used to estimate its standard error with matching stratum membership as the clustering variable.
>
> The estimated effect was \$`r round(est$estimate)` (SE = `r round(est$std.error, 1)`, p = `r round(est$p.value, 3)`), indicating that the average effect of the treatment for those who received it is to increase earnings.
diff --git a/vignettes/assessing-balance.Rmd b/vignettes/assessing-balance.Rmd
index 7a51463..a227673 100644
--- a/vignettes/assessing-balance.Rmd
+++ b/vignettes/assessing-balance.Rmd
@@ -52,7 +52,7 @@ Common recommendations for assessing balance include the following:
- **Variance Ratios**. The variance ratio is the ratio of the variance of a covariate in one group to that in the other. Variance ratios close to 1 indicate good balance because they imply the variances of the samples are similar [@austin2009].
-- **Empirical CDF Statistics**. Statistics related to the difference in the empirical cumulative density functions (eCDFs) of each covariate between groups allow assessment of imbalance across the entire covariate distribution of that covariate rather than just its mean or variance. The maximum eCDF difference, also known as the Kolmogorov-Smirnov statistic, is sometimes recommended as a useful supplement to SMDs for assessing balance [@austin2015] and is often used as a criterion to use in propensity score methods that attempt to optimize balance [e.g., @mccaffrey2004; @diamond2013]. Although the mean eCDF difference has not been as well studied, it provides a summary of imbalance that may be missed by relying solely on the maximum difference.
+- **Empirical CDF Statistics**. Statistics related to the difference in the empirical cumulative distribution functions (eCDFs) of each covariate between groups allow assessment of imbalance across the entire covariate distribution of that covariate rather than just its mean or variance. The maximum eCDF difference, also known as the Kolmogorov-Smirnov statistic, is sometimes recommended as a useful supplement to SMDs for assessing balance [@austin2015] and is often used as a criterion to use in propensity score methods that attempt to optimize balance [e.g., @mccaffrey2004; @diamond2013]. Although the mean eCDF difference has not been as well studied, it provides a summary of imbalance that may be missed by relying solely on the maximum difference.
- **Visual Diagnostics**. Visual diagnostics such as eCDF plots, empirical quantile-quantile (eQQ) plots, and kernel density plots can be used to see exactly how the covariate distributions differ from each other, i.e., where in the distribution the greatest imbalances are [@ho2007; @austin2009]. This can help to figure out how to tailor a matching method to target imbalance in a specific region of the covariate distribution.
diff --git a/vignettes/estimating-effects.Rmd b/vignettes/estimating-effects.Rmd
index 22e71ac..ed0fded 100644
--- a/vignettes/estimating-effects.Rmd
+++ b/vignettes/estimating-effects.Rmd
@@ -32,7 +32,8 @@ me_ok <- requireNamespace("marginaleffects", quietly = TRUE) &&
requireNamespace("sandwich", quietly = TRUE)
su_ok <- requireNamespace("survival", quietly = TRUE)
boot_ok <- requireNamespace("boot", quietly = TRUE)
-
+```
+```{r, include = FALSE}
#Generating data similar to Austin (2009) for demonstrating treatment effect estimation
gen_X <- function(n) {
X <- matrix(rnorm(9 * n), nrow = n, ncol = 9)
@@ -107,7 +108,7 @@ This guide is structured as follows: first, information on the concepts related
Before an effect is estimated, the estimand must be specified and clarified. Although some aspects of the estimand depend not only on how the effect is estimated after matching but also on the matching method itself, other aspects must be considered at the time of effect estimation and interpretation. Here, we consider three aspects of the estimand: the population the effect is meant to generalize to (the target population), the effect measure, and whether the effect is marginal or conditional.
-**The target population.** Different matching methods allow you to estimate effects that can generalize to different target populations. The most common estimand in matching the average treatment effect in the treated (ATT), which is the average effect of treatment for those who receive treatment. This estimand is estimable for matching methods that do not change the treated units (i.e., by weighting or discarding units) and is requested in `matchit()` by setting `estimand = "ATT"` (which is the default). The average treatment effect in the population (ATE) is the average effect of treatment for the population from which the sample is a random sample. This estimand is estimable only for methods that allow the ATE and either do not discard units from the sample or explicit target full sample balance, which in `MatchIt` is limited to full matching, subclassification, and template matching when setting `estimand = "ATE"`. When treated units are discarded (e.g., through the use of common support restrictions, calipers, cardinality matching, or [coarsened] exact matching), the estimand corresponds to neither the population ATT nor the population ATE, but rather to an average treatment effect in the remaining matched sample (ATM), which may not correspond to any specific target population. See @greiferChoosingEstimandWhen2021 for a discussion on the substantive considerations involved when choosing the target population of the estimand.
+**The target population.** Different matching methods allow you to estimate effects that can generalize to different target populations. The most common estimand in matching is the average treatment effect in the treated (ATT), which is the average effect of treatment for those who receive treatment. This estimand is estimable for matching methods that do not change the treated units (i.e., by weighting or discarding units) and is requested in `matchit()` by setting `estimand = "ATT"` (which is the default). The average treatment effect in the population (ATE) is the average effect of treatment for the population from which the sample is a random sample. This estimand is estimable only for methods that allow the ATE and either do not discard units from the sample or explicit target full sample balance, which in `MatchIt` is limited to full matching, subclassification, and template matching when setting `estimand = "ATE"`. When treated units are discarded (e.g., through the use of common support restrictions, calipers, cardinality matching, or [coarsened] exact matching), the estimand corresponds to neither the population ATT nor the population ATE, but rather to an average treatment effect in the remaining matched sample (ATM), which may not correspond to any specific target population. See @greiferChoosingEstimandWhen2021 for a discussion on the substantive considerations involved when choosing the target population of the estimand.
**Marginal and conditional effects.** A marginal effect is a comparison between the expected potential outcome under treatment and the expected potential outcome under control. This is the same quantity estimated in randomized trials without blocking or covariate adjustment and is particularly useful for quantifying the overall effect of a policy or population-wide intervention. A conditional effect is the comparison between the expected potential outcomes in the treatment groups within strata. This is useful for identifying the effect of a treatment for an individual patient or a subset of the population.
@@ -145,7 +146,7 @@ To compute SEs after g-computation, a method known as the delta method is used;
**Robust standard errors.** Also known as sandwich SEs (due to the form of the formula for computing them), heteroscedasticity-consistent SEs, or Huber-White SEs, robust SEs are an adjustment to the usual maximum likelihood or ordinary least squares SEs that are robust to violations of some of the assumptions required for usual SEs to be valid [@mackinnon1985]. Although there has been some debate about their utility [@king2015], robust SEs rarely degrade inferences and often improve them. Generally, robust SEs **must** be used when any non-uniform weights are included in the estimation (e.g., with matching with replacement or inverse probability weighting).
-**Cluster-robust standard errors.** A version of robust SEs known as cluster-robust SEs [@liang1986] can be used to account for dependence between observations within clusters (e.g., matched pairs). @abadie2019 demonstrate analytically that cluster-robust SEs are generally valid after matching, whereas regular robust SEs can over- or under-estimate the true sampling variability of the effect estimator depending on the specification of the outcome model (if any) and degree of effect modification. A plethora of simulation studies have further confirmed the validity of cluster-robust SEs after matching [e.g., @austin2009a; @austin2014; @gayat2012; @wan2019; @austin2013]. Given this evidence favoring the use of cluster-robust SEs, we recommend them in most cases and use them judiciously in the this guide[^1].
+**Cluster-robust standard errors.** A version of robust SEs known as cluster-robust SEs [@liang1986] can be used to account for dependence between observations within clusters (e.g., matched pairs). @abadie2019 demonstrate analytically that cluster-robust SEs are generally valid after matching, whereas regular robust SEs can over- or under-estimate the true sampling variability of the effect estimator depending on the specification of the outcome model (if any) and degree of effect modification. A plethora of simulation studies have further confirmed the validity of cluster-robust SEs after matching [e.g., @austin2009a; @austin2014; @gayat2012; @wan2019; @austin2013]. Given this evidence favoring the use of cluster-robust SEs, we recommend them in most cases and use them judiciously in this guide[^1].
[^1]: Because they are only appropriate with a large number of clusters, cluster-robust SEs are generally not used with subclassification methods. Regular robust SEs are valid with these methods when using the subclassification weights to estimate marginal effects.
@@ -173,7 +174,7 @@ head(d)
We will need to the following packages to perform the desired analyses:
-- `marginaleffects` provides the `comparisons()` function for performing g-computation and estimating the SEs and confidence intervals of the average estimate potential outcomes and treatment effects
+- `marginaleffects` provides the `avg_comparisons()` function for performing g-computation and estimating the SEs and confidence intervals of the average estimate potential outcomes and treatment effects
- `sandwich` is used internally by `marginaleffects` to compute robust and cluster-robust SEs
- `survival` provides `coxph()` to estimate the coefficients in a Cox-proportional hazards model for the marginal hazard ratio, which we will use for survival outcomes.
@@ -187,7 +188,7 @@ library("MatchIt")
library("marginaleffects")
```
-All effect estimates will be computed using `marginaleffects::comparions()`, even when its use may be superfluous (e.g., for performing a t-test in the matched set). As previously mentioned, this is because it is useful to have a single workflow that works no matter the situation, perhaps with very slight modifications to accommodate different contexts. Using `comparisons()` has several advantages, even when the alternatives are simple: it only provides the effect estimate, and not other coefficients; it automatically incorporates robust and cluster-robust SEs if requested; and it always produces average marginal effects for the correct population if requested.
+All effect estimates will be computed using `marginaleffects::avg_comparions()`, even when its use may be superfluous (e.g., for performing a t-test in the matched set). As previously mentioned, this is because it is useful to have a single workflow that works no matter the situation, perhaps with very slight modifications to accommodate different contexts. Using `avg_comparions()` has several advantages, even when the alternatives are simple: it only provides the effect estimate, and not other coefficients; it automatically incorporates robust and cluster-robust SEs if requested; and it always produces average marginal effects for the correct population if requested.
Other packages may be of use but are not used here. There are alternatives to the `marginaleffects` package for computing average marginal effects, including `margins` and `stdReg`. The `survey` package can be used to estimate robust SEs incorporating weights and provides functions for survey-weighted generalized linear models and Cox-proportional hazards models. It is often used with propensity score weighting.
@@ -229,7 +230,7 @@ fit1 <- lm(Y_C ~ A * (X1 + X2 + X3 + X4 + X5 +
data = md, weights = weights)
```
-Next, we use `marginaleffects::comparisons()` to estimate the ATT.
+Next, we use `marginaleffects::avg_comparisons()` to estimate the ATT.
```{r, eval=me_ok}
avg_comparisons(fit1, variables = "A",
@@ -238,21 +239,20 @@ avg_comparisons(fit1, variables = "A",
wts = "weights")
```
-Let's break down the call to `avg_comparisons()`: to the first argument, we supply the model fit, `fit`; to the `variables` argument, the name of the treatment (`"A"`); to the `vcov` argument, a formula with subclass membership (`~subclass`) to request cluster-robust SEs; to the `newdata` argument, a version of the matched dataset containing only the treated units (`subset(md, A == 1)`) to request the ATT; and to the `wts` argument, the names of the variable in `md` containing the matching weights (`"weights"`) to ensure they are included in the analysis. Some of these arguments differ depending on the specifics of the matching method and outcome type; see the sections below for information.
+Let's break down the call to `avg_comparisons()`: to the first argument, we supply the model fit, `fit1`; to the `variables` argument, the name of the treatment (`"A"`); to the `vcov` argument, a formula with subclass membership (`~subclass`) to request cluster-robust SEs; to the `newdata` argument, a version of the matched dataset containing only the treated units (`subset(md, A == 1)`) to request the ATT; and to the `wts` argument, the names of the variable in `md` containing the matching weights (`"weights"`) to ensure they are included in the analysis. Some of these arguments differ depending on the specifics of the matching method and outcome type; see the sections below for information.
If, in addition to the effect estimate, we want the average estimated potential outcomes, we can use `marginaleffects::avg_predictions()`, which we demonstrate below. Note the interpretation of the resulting estimates as the expected potential outcomes is only valid if all covariates present in the outcome model (if any) are interacted with the treatment.
-```{r, eval=me_ok && packageVersion("marginaleffects") > "0.10.0"}
+```{r, eval=me_ok && packageVersion("marginaleffects") >= "0.11.0"}
avg_predictions(fit1, variables = "A",
vcov = ~subclass,
newdata = subset(md, A == 1),
- wts = "weights",
- by = "A")
+ wts = "weights")
```
-We can see that the difference in potential outcome means is equal to the average treatment effect computed previously[^4]. Almost all of the arguments to `avg_predictions()` are the same as those to `avg_comparisons()`; the only difference is that the `by` argument needs to be specified naming the treatment. (Note: due to a bug, the above code will not evaluate unless `marginaleffects` is a version greater than 0.10.0.)
+We can see that the difference in potential outcome means is equal to the average treatment effect computed previously[^4]. All of the arguments to `avg_predictions()` are the same as those to `avg_comparisons()`.
-[^4]: To verify that they are equal, add the argument `hypothesis = "revpairwise"` to the call to `predictions()`; this explicitly compares the average potential outcomes and should yield identical estimates to the `comparisons()` call.
+[^4]: To verify that they are equal, supply the output of `avg_predictions()` to `hypotheses(), e.g., `avg_predictions(...) |> hypotheses("revpairwise")`; this explicitly compares the average potential outcomes and should yield identical estimates to the `avg_comparisons()` call.
### Adjustments to the Standard Case
@@ -266,7 +266,7 @@ When matching for the ATE (including [coarsened] exact matching, full matching,
When matching with replacement (i.e., nearest neighbor or genetic matching with `replace = TRUE`), effect and SE estimation need to account for control unit multiplicity (i.e., repeated use) and within-pair correlations [@hill2006; @austin2020a]. Although @abadie2008 demonstrated analytically that bootstrap SEs may be invalid for matching with replacement, simulation work by @hill2006 and @bodory2020 has found that bootstrap SEs are adequate and generally slightly conservative. See the section "Using Bootstrapping to Estimate Confidence Intervals" for instructions on using the bootstrap and an example that use matching with replacement.
-Because control units do not belong to unique pairs, there is no pair membership in the `match.data()` output. One can simply change `vcov = ~subclass` to `vcov = "HC3"` in the calls to `comparisons()` and `predictions()` to use robust SEs instead of cluster-robust SEs, as recommended by @hill2006. There is some evidence for an alternative approach that incorporates pair membership and adjusts for reuse of control units, though this has only been studied for survival outcomes [@austin2020a]. This adjustment involves using two-way cluster-robust SEs with pair membership and unit ID as the clustering variables. For continuous and binary outcomes, this involves the following two changes: 1) replace `match.data()` with `get_matches()`, which produces a dataset with a row per unit per pair, meaning control units matched to multiple treated units will appear multiple times in the dataset; 2) set `vcov = ~subclass + id` in the calls to `avg_comparisons()` and `avg_predictions()`. For survival outcomes, a special procedure must be used; see the section on survival outcomes below.
+Because control units do not belong to unique pairs, there is no pair membership in the `match.data()` output. One can simply change `vcov = ~subclass` to `vcov = "HC3"` in the calls to `comparisons()` and `predictions()` to use robust SEs instead of cluster-robust SEs, as recommended by @hill2006. There is some evidence for an alternative approach that incorporates pair membership and adjusts for reuse of control units, though this has only been studied for survival outcomes [@austin2020a]. This adjustment involves using two-way cluster-robust SEs with pair membership and unit ID as the clustering variables. For continuous and binary outcomes, this involves the following two changes: 1) replace `match.data()` with `get_matches()`, which produces a dataset with one row per unit per pair, meaning control units matched to multiple treated units will appear multiple times in the dataset; 2) set `vcov = ~subclass + id` in the calls to `avg_comparisons()` and `avg_predictions()`. For survival outcomes, a special procedure must be used; see the section on survival outcomes below.
#### Matching without pairing
@@ -313,7 +313,7 @@ To fit a logistic regression model, change `lm()` to `glm()` and set `family = q
[^7]: Note that for low or high average expected risks computed with `predictions()`, the confidence intervals may go below 0 or above 1; this is because an approximation is used. To avoid this problem, bootstrapping or simulation-based inference can be used instead.
-To compute the marginal RR, we need to add `transform_pre = "lnratioavg"` to `avg_comparisons()`; this computes the marginal log RR. To get the marginal RR, we need to add `transform_post = "exp"` to `avg_comparisons()`, which exponentiates the marginal log RR and its confidence interval. The code below computes the effects and displays the statistics of interest:
+To compute the marginal RR, we need to add `comparison = "lnratioavg"` to `avg_comparisons()`; this computes the marginal log RR. To get the marginal RR, we need to add `transform = "exp"` to `avg_comparisons()`, which exponentiates the marginal log RR and its confidence interval. The code below computes the effects and displays the statistics of interest:
```{r, eval=me_ok}
#Logistic regression model with covariates
@@ -328,13 +328,13 @@ avg_comparisons(fit2,
vcov = ~subclass,
newdata = subset(md, A == 1),
wts = "weights",
- transform_pre = "lnratioavg",
- transform_post = "exp")
+ comparison = "lnratioavg",
+ transform = "exp")
```
-The output displays the marginal RR, its Z-value, the p-value for the Z-test against 0, and its confidence interval. (Note that even though the `Contrast` label still suggests the log risk ratio, the risk ratio is actually displayed. You can confirm this by manually exponentiating the log risk ratio computed just before.) To view the log RR and its standard error, omit the `transform_post` argument.
+The output displays the marginal RR, its Z-value, the p-value for the Z-test of the log RR against 0, and its confidence interval. (Note that even though the `Contrast` label still suggests the log RR, the RR is actually displayed.) To view the log RR and its standard error, omit the `transform` argument.
-For the marginal OR, the only thing that needs to change is that `transform_pre` should be set to `"lnoravg"`.
+For the marginal OR, the only thing that needs to change is that `comparison` should be set to `"lnoravg"`.
#### Survival outcomes
@@ -396,7 +396,7 @@ The bootstrap is an alternative to the delta method for estimating confidence in
For the standard bootstrap, we need a function that takes in the original dataset and a vector of sampled unit indices and returns the estimated quantity of interest. This function should perform the matching on the bootstrap sample, fit the outcome model, and estimate the treatment effect using g-computation. In this example, we'll use matching with replacement, since the standard bootstrap has been found to work well with it [@bodory2020; @hill2006], despite some analytic results recommending otherwise [@abadie2008]. We'll implement g-computation manually rather than using `avg_comparisons()`, as this dramatically improves the speed of the estimation since we don't require standard errors to be estimated in each sample (or other processing `avg_comparisons()` does). We'll consider the marginal RR ATT of `A` on the binary outcome `Y_B`.
-The first step is to write the estimation function, we we call `boot_fun`. This function returns the marginal RR. In it, we perform the matching, estimate the effect, and return the estimate of interest.
+The first step is to write the estimation function, we call `boot_fun`. This function returns the marginal RR. In it, we perform the matching, estimate the effect, and return the estimate of interest.
```{r}
boot_fun <- function(data, i) {
diff --git a/vignettes/matching-methods.Rmd b/vignettes/matching-methods.Rmd
index 5ec18ba..ac6483a 100644
--- a/vignettes/matching-methods.Rmd
+++ b/vignettes/matching-methods.Rmd
@@ -155,7 +155,7 @@ To perform Mahalanobis distance matching without the need to estimate or use a p
### Exact matching (`exact`)
-To perform exact matching on all supplied covariates, the `method` argument can be set to `"exact"`. To perform exact matching only on some covariates and some other form of matching within exact matching strata on other covariates, the `exact` argument can be used. Covariates supplied to the `exact` argument will be matched exactly, and the form of matching specified by `method` (e.g., `"nearest"` for nearest neighbor matching) will take place within each exact matching stratum. This can be a good way to gain some of the benefits of exact matching without completely succumbing to the curse of dimensionality. As with exact matching performed with `method = "exact"`, any units in strata lacking members of one of the treatment groups will be left unmatched. Note that although matching occurs within each exact matching stratum, propensity score estimation and computation of the Mahalanobis distance occur in the full sample. **If units from the treated group are unmatched due to an exact matching restriction, the estimand no longer corresponds to the ATT.**
+To perform exact matching on all supplied covariates, the `method` argument can be set to `"exact"`. To perform exact matching only on some covariates and some other form of matching within exact matching strata on other covariates, the `exact` argument can be used. Covariates supplied to the `exact` argument will be matched exactly, and the form of matching specified by `method` (e.g., `"nearest"` for nearest neighbor matching) will take place within each exact matching stratum. This can be a good way to gain some of the benefits of exact matching without completely succumbing to the curse of dimensionality. As with exact matching performed with `method = "exact"`, any units in strata lacking members of one of the treatment groups will be left unmatched. Note that although matching occurs within each exact matching stratum, propensity score estimation and computation of the Mahalanobis or other distance matrix occur in the full sample. **If units from the treated group are unmatched due to an exact matching restriction, the estimand no longer corresponds to the ATT.**
### Anti-exact matching (`antiexact`)
@@ -171,7 +171,11 @@ The `reuse.max` argument can also be used with `method = "nearest"` to control h
The most common form of matching, 1:1 matching, involves pairing one control unit with each treated unit. To perform $k$:1 matching (e.g., 2:1 or 3:1), which pairs (up to) $k$ control units with each treated unit, the `ratio` argument can be specified. Performing $k$:1 matching can preserve precision by preventing too many control units from being unmatched and dropped from the matched sample, though the gain in precision by increasing $k$ diminishes rapidly after 4 [@rosenbaum2020]. Importantly, for $k>1$, the matches after the first match will generally be worse than the first match in terms of closeness to the treated unit, so increasing $k$ can also worsen balance. @austin2010a found that 1:1 or 1:2 matching generally performed best in terms of mean squared error. In general, it makes sense to use higher values of $k$ while ensuring that balance is satisfactory.
-With nearest neighbor and optimal pair matching, variable $k$:1 matching, in which the number of controls matched to each treated unit varies, can also be used; this can have improved performance over "fixed" $k$:1 matching [@ming2000]. See `?method_nearest` and `?method_optimal` for information on implementing variable $k$:1 matching.
+With nearest neighbor and optimal pair matching, variable $k$:1 matching, in which the number of controls matched to each treated unit varies, can also be used; this can have improved performance over "fixed" $k$:1 matching [@ming2000; @rassenOnetomanyPropensityScore2012]. See `?method_nearest` and `?method_optimal` for information on implementing variable $k$:1 matching.
+
+### Matching order (`m.order`)
+
+For nearest neighbor matching (including genetic matching), units are matched in an order, and that order can affect the quality of individual matches and of the resulting matched sample. With `method = "nearest"`, the allowable options to `m.order` to control the matching order are `"largest"`, `"smallest"`, `"closest"`, `"random"`, and `"data"`. With `method = "genetic"`, all but `"closest"` can be used. Requesting `"largest"` means that treated units with the largest propensity scores, i.e., those least like the control units, will be matched first, which prevents them from having bad matches after all the close control units have been used up. `"smallest"` means that treated units with the smallest propensity scores are matched first. `"closest"` means that potential pairs with the smallest distance between units will be matched first, which ensures that the best possible matches are included in the matched sample but can yield poor matches for units whose best match is far from them; this makes it particularly useful when matching with a caliper. `"random"` matches in a random order and `"data"` matches in order of the data. A propensity score is required for `"largest"` and `"smallest"` but not for the other options. @rubin1973 recommends using `"largest"` or `"random"`, though @austin2013b recommends against `"largest"` and instead favors `"closest"` or `"random"`.
## Choosing a Matching Method
diff --git a/vignettes/references.bib b/vignettes/references.bib
index 59d9534..d2cc7fb 100644
--- a/vignettes/references.bib
+++ b/vignettes/references.bib
@@ -1640,3 +1640,18 @@
file = {/Users/NoahGreifer/Zotero/storage/PFFUW2AF/Cohn and Zubizarreta - 2021 - Profile Matching for the Generalization and Person.pdf;/Users/NoahGreifer/Zotero/storage/QX8CY5HV/Cohn and Zubizarreta - 2021 - Profile Matching for the Generalization and Person.pdf}
}
+
+@article{rassenOnetomanyPropensityScore2012,
+ title = {One-to-many propensity score matching in cohort studies},
+ author = {Rassen, Jeremy A. and Shelat, Abhi A. and Myers, Jessica and Glynn, Robert J. and Rothman, Kenneth J. and Schneeweiss, Sebastian},
+ year = {2012},
+ date = {2012},
+ journal = {Pharmacoepidemiology and Drug Safety},
+ pages = {69--80},
+ volume = {21},
+ number = {S2},
+ doi = {10.1002/pds.3263},
+ url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/pds.3263},
+ note = {{\_}eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pds.3263},
+ langid = {en}
+}