New Upstream Snapshot - r-cran-phangorn
Ready changes
Summary
Merged new upstream version: 2.11.1+git20230125.1.bac1456+dfsg (was: 2.11.1+dfsg).
Resulting package
Built on 2023-01-27T09:24 (took 9m41s)
The resulting binary packages can be installed (if you have the apt repository enabled) by running one of:
apt install -t fresh-snapshots r-cran-phangorn-dbgsymapt install -t fresh-snapshots r-cran-phangorn
Diff
diff --git a/DESCRIPTION b/DESCRIPTION
index 971ee6b..e9af9ea 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: phangorn
Title: Phylogenetic Reconstruction and Analysis
-Version: 2.11.1
+Version: 2.12.0.900
Authors@R:
c(person(given = "Klaus",
family = "Schliep",
@@ -84,10 +84,11 @@ VignetteBuilder: knitr, utils
biocViews: Software, Technology, QualityControl
Encoding: UTF-8
Repository: CRAN
+Roxygen: list(old_usage = TRUE)
RoxygenNote: 7.2.3
Language: en-US
NeedsCompilation: yes
-Packaged: 2023-01-22 23:45:05 UTC; klaus
+Packaged: 2023-01-27 09:19:14 UTC; root
Author: Klaus Schliep [aut, cre] (<https://orcid.org/0000-0003-2941-0161>),
Emmanuel Paradis [aut] (<https://orcid.org/0000-0003-3092-2199>),
Leonardo de Oliveira Martins [aut]
@@ -108,4 +109,3 @@ Author: Klaus Schliep [aut, cre] (<https://orcid.org/0000-0003-2941-0161>),
Joseph Brown [ctb] (<https://orcid.org/0000-0002-3835-8062>),
Santiago Claramunt [ctb] (<https://orcid.org/0000-0002-8926-5974>)
Maintainer: Klaus Schliep <klaus.schliep@gmail.com>
-Date/Publication: 2023-01-23 10:10:02 UTC
diff --git a/MD5 b/MD5
deleted file mode 100644
index ff9d401..0000000
--- a/MD5
+++ /dev/null
@@ -1,268 +0,0 @@
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-a09a6083ee44ca91ebf21588f7c0c073 *man/pml_bb.Rd
-f80cfdf44cae9a09939e75d72a4222af *man/read.aa.Rd
-d22f32912c219db0ea096add5fe1eb81 *man/read.nexus.splits.Rd
-133c5bb904032971f0b5e69afcfe11c9 *man/read.phyDat.Rd
-26ad4423a6fd8650b8bf599597615558 *man/reexports.Rd
-03065d6d8d2fdbfed0e1fce7561277fc *man/simSeq.Rd
-56615aed5311ba4bd692ca7ce6adb1c6 *man/splitsNetwork.Rd
-a33948d6c5b8dd8e1ee6fa32e87cdd11 *man/superTree.Rd
-a661c6d4a0f5208e1574d8221c28c23f *man/transferBootstrap.Rd
-4ccd0c490c4a6e352971180ccda0240f *man/treedist.Rd
-73308bbdef266b537f9a6aa413f539b7 *man/upgma.Rd
-8d1ad5757e44f5318adabe5b0d94a2e1 *man/writeDist.Rd
-7b4fdcfc85bc945a86723f094057dbcb *man/yeast.Rd
-427f79cb72f331e85c03454d5af3f8fe *src/Fitch.h
-c45a68c7cfa876c9f79acf87d0809a57 *src/Makevars
-b1f8a3fd70020ba7cf078bc586eac493 *src/RcppExports.cpp
-dc3cbbe97834227fc3ac5456f58848b1 *src/dist.c
-a96e879a51643d022766029e97213bb6 *src/dupAtomMat.cpp
-59243492d612c0989e8ef4d00be4b6b0 *src/fitch64.cpp
-403b0f1d27a423e357f3e96cd7aefe12 *src/lessAndEqual.h
-8864a57291c639d781c4684ed924e654 *src/ml.c
-6f7f95f69b4d259fdc28d8a87f144fd2 *src/phangorn.c
-66922c256930b250098ba2d60e12a6b2 *src/phangorn_utils.cpp
-e19952320d4d6bd26c1b95080a7b5d0b *src/phangorn_utils.h
-b322aa12ac146cbcdd5d9568a7fdb18b *src/rcSet.h
-be65a5e85d58c56ab754b21292ab5360 *src/sankoff.c
-1f91da56a713aeb7bf1f0be4a19edadd *src/sprdist.c
-3a302c7e7c5126983bbc108aae9b63e2 *tests/tinytest.R
-f98f253b2e4d087b4cdc6f7a0819b337 *vignettes/AF.RData
-46b7113923dc06e460bf992618388e1a *vignettes/AdvancedFeatures.Rmd
-6f34d5bd0b9cbcbaa5752306a6d0176f *vignettes/Ancestral.Rmd
-fe8fb6c254f2f02f37867ab53df0d941 *vignettes/IntertwiningTreesAndNetworks.Rmd
-591c6be6c9e5bfcd31b7775346ae1d6c *vignettes/MLbyHand.Rmd
-150d4e10a4611040171554e0424d7d56 *vignettes/Morphological.Rmd
-69f9f3b50e234548e32cf8274cdc1c74 *vignettes/Networx.Rmd
-6537f663557a42d79e140b6ad683a329 *vignettes/Trees.RData
-f6cdcf0f44a3ec84c98cc4e768ae982b *vignettes/Trees.Rmd
-3b1285249eed24e70427c0b56450533a *vignettes/cophylo.png
-1ca8b1d97a011a9d0ecd9630d548dfb3 *vignettes/movie.gif
-a0c9036e26bf0b3fae480be0ffe40241 *vignettes/phangorn.bib
-6b0ad41d4ea31b2e41c1b131c538c69d *vignettes/seqLogo.png
diff --git a/NEWS b/NEWS
index a97e75e..b08c067 100644
--- a/NEWS
+++ b/NEWS
@@ -1,3 +1,20 @@
+ CHANGES in PHANGORN VERSION 2.12.0
+
+NEW FEATURES
+
+ o pml.control got an argument statefreq, which controls if "empirical"
+
+ or (ML) "estimated" state/base frequencies are used/computed. Speeds up
+
+ modelTest and pml_bb/optim.pml. For large state space (amino acids,
+
+ codons) and small datasets the "estimated" might not be very reliable.
+
+OTHER CHANGES
+
+BUG FIXES
+
+
CHANGES in PHANGORN VERSION 2.11.0
OTHER CHANGES
diff --git a/R/modelTest.R b/R/modelTest.R
index 7a21b52..fcf5cac 100644
--- a/R/modelTest.R
+++ b/R/modelTest.R
@@ -4,8 +4,6 @@ aic.weights <- function(aic) {
}
-
-
#' ModelTest
#'
#' Comparison of different nucleotide or amino acid substitution models
@@ -124,36 +122,23 @@ modelTest <- function(object, tree = NULL, model = NULL, G = TRUE, I = TRUE,
data.frame(c("Model", "df", "logLik", "AIC", "AICc", "BIC"))
calls <- vector("list", n)
trees <- vector("list", n)
-# if (trace > 0) print(model)
fittmp <- optim.pml(fit, model = model, control = control)
res[m, 1] <- model
pars <- get_pars(fittmp, nseq)
res[m, 2:6] <- pars
if (trace > 0) cat(formatC(res[m,1], width=12),
prettyNum(pars[-4], preserve.width="individual"), "\n")
-# res[m, 2] <- fittmp$df
-# res[m, 3] <- fittmp$logLik
-# res[m, 4] <- AIC(fittmp)
-# res[m, 5] <- AICc(fittmp)
-# res[m, 6] <- AIC(fittmp, k = log(nseq))
calls[[m]] <- fittmp$call
-
trees[[m]] <- fittmp$tree
m <- m + 1
if (I) {
-# if (trace > 0) print(paste0(model, "+I"))
fitI <- optim.pml(fittmp, model = model, optInv = TRUE,
control = control)
res[m, 1] <- paste0(model, "+I")
pars <- get_pars(fitI, nseq)
res[m, 2:6] <- pars
- if (trace > 0) cat(formatC(res[m,1], width=12), prettyNum(pars[-4], preserve.width="individual"),
- "\n")
-# res[m, 2] <- fitI$df
-# res[m, 3] <- fitI$logLik
-# res[m, 4] <- AIC(fitI)
-# res[m, 5] <- AICc(fitI)
-# res[m, 6] <- AIC(fitI, k = log(nseq))
+ if (trace > 0) cat(formatC(res[m,1], width=12), prettyNum(pars[-4],
+ preserve.width="individual"), "\n")
calls[[m]] <- fitI$call
trees[[m]] <- fitI$tree
m <- m + 1
@@ -166,88 +151,59 @@ modelTest <- function(object, tree = NULL, model = NULL, G = TRUE, I = TRUE,
res[m, 1] <- paste0(model, "+G(", k, ")")
pars <- get_pars(fitG, nseq)
res[m, 2:6] <- pars
- if (trace > 0) cat(formatC(res[m,1], width=12), prettyNum(pars[-4], preserve.width="individual"),
- "\n")
-# res[m, 2] <- fitG$df
-# res[m, 3] <- fitG$logLik
-# res[m, 4] <- AIC(fitG)
-# res[m, 5] <- AICc(fitG)
-# res[m, 6] <- AIC(fitG, k = log(nseq))
+ if (trace > 0) cat(formatC(res[m,1], width=12), prettyNum(pars[-4],
+ preserve.width="individual"), "\n")
calls[[m]] <- fitG$call
trees[[m]] <- fitG$tree
m <- m + 1
}
if (G & I) {
-# if (trace > 0) print(paste0(model, "+G+I"))
fitGI <- update(fitI, k = k)
fitGI <- optim.pml(fitGI, model = model, optGamma = TRUE,
optInv = TRUE, control = control)
res[m, 1] <- paste0(model, "+G(", k, ")+I")
pars <- get_pars(fitGI, nseq)
res[m, 2:6] <- pars
- if (trace > 0) cat(formatC(res[m,1], width=12), prettyNum(pars[-4], preserve.width="individual"),
- "\n")
-# res[m, 2] <- fitGI$df
-# res[m, 3] <- fitGI$logLik
-# res[m, 4] <- AIC(fitGI)
-# res[m, 5] <- AICc(fitGI)
-# res[m, 6] <- AIC(fitGI, k = log(nseq))
+ if (trace > 0) cat(formatC(res[m,1], width=12), prettyNum(pars[-4],
+ preserve.width="individual"), "\n")
calls[[m]] <- fitGI$call
trees[[m]] <- fitGI$tree
m <- m + 1
}
if (FREQ) {
-# if (trace > 0) print(paste0(model, "+F"))
fitF <- optim.pml(fittmp, model = model, optBf = TRUE,
control = control)
res[m, 1] <- paste0(model, "+F")
pars <- get_pars(fitF, nseq)
res[m, 2:6] <- pars
- if (trace > 0) cat(formatC(res[m,1], width=12), prettyNum(pars[-4], preserve.width="individual"),
- "\n")
-# res[m, 2] <- fitF$df
-# res[m, 3] <- fitF$logLik
-# res[m, 4] <- AIC(fitF)
- # res[m, 5] <- AICc(fitF)
- # res[m, 6] <- AIC(fitF, k = log(nseq))
+ if (trace > 0) cat(formatC(res[m,1], width=12), prettyNum(pars[-4],
+ preserve.width="individual"), "\n")
calls[[m]] <- fitF$call
trees[[m]] <- fitF$tree
m <- m + 1
}
if (FREQ & I) {
-# if (trace > 0) print(paste0(model, "+I+F"))
fitIF <- update(fitF, inv = fitI$inv)
fitIF <- optim.pml(fitIF, model = model, optBf = TRUE, optInv = TRUE,
control = control)
res[m, 1] <- paste0(model, "+I+F")
pars <- get_pars(fitIF, nseq)
res[m, 2:6] <- pars
- if (trace > 0) cat(formatC(res[m,1], width=12), prettyNum(pars[-4], preserve.width="individual"),
- "\n")
-# res[m, 2] <- fitIF$df
-# res[m, 3] <- fitIF$logLik
-# res[m, 4] <- AIC(fitIF)
-# res[m, 5] <- AICc(fitIF)
-# res[m, 6] <- AIC(fitIF, k = log(nseq))
+ if (trace > 0) cat(formatC(res[m,1], width=12), prettyNum(pars[-4],
+ preserve.width="individual"), "\n")
calls[[m]] <- fitIF$call
trees[[m]] <- fitIF$tree
m <- m + 1
}
if (FREQ & G) {
-# if (trace > 0) print(paste0(model, "+G+F"))
fitGF <- update(fitF, k = k, shape = fitG$shape)
fitGF <- optim.pml(fitGF, model = model, optBf = TRUE,
optGamma = TRUE, control = control)
res[m, 1] <- paste0(model, "+G(", k, ")+F")
pars <- get_pars(fitGF, nseq)
res[m, 2:6] <- pars
- if (trace > 0) cat(formatC(res[m,1], width=12), prettyNum(pars[-4], preserve.width="individual"),
- "\n")
-# res[m, 2] <- fitGF$df
-# res[m, 3] <- fitGF$logLik
-# res[m, 4] <- AIC(fitGF)
-# res[m, 5] <- AICc(fitGF)
-# res[m, 6] <- AIC(fitGF, k = log(nseq))
+ if (trace > 0) cat(formatC(res[m,1], width=12), prettyNum(pars[-4],
+ preserve.width="individual"), "\n")
calls[[m]] <- fitGF$call
trees[[m]] <- fitGF$tree
m <- m + 1
@@ -260,13 +216,8 @@ modelTest <- function(object, tree = NULL, model = NULL, G = TRUE, I = TRUE,
res[m, 1] <- paste0(model, "+G(", k, ")+I+F")
pars <- get_pars(fitGIF, nseq)
res[m, 2:6] <- pars
- if (trace > 0) cat(formatC(res[m,1], width=12), prettyNum(pars[-4], preserve.width="individual"),
- "\n")
-# res[m, 2] <- fitGIF$df
-# res[m, 3] <- fitGIF$logLik
-# res[m, 4] <- AIC(fitGIF)
-# res[m, 5] <- AICc(fitGIF)
-# res[m, 6] <- AIC(fitGIF, k = log(nseq))
+ if (trace > 0) cat(formatC(res[m,1], width=12), prettyNum(pars[-4],
+ preserve.width="individual"), "\n")
calls[[m]] <- fitGIF$call
trees[[m]] <- fitGIF$tree
m <- m + 1
diff --git a/R/phylo.R b/R/phylo.R
index 34e9e6c..0702d86 100644
--- a/R/phylo.R
+++ b/R/phylo.R
@@ -2131,6 +2131,10 @@ optim.pml <- function(object, optNni = FALSE, optBf = FALSE, optQ = FALSE,
object <- update.pml(object, Q = Q)
}
}
+ if(optBf && control$statefreq=="empirical" && type != "CODON"){
+ bf <- baseFreq(data)
+ object <- update.pml(object, bf = bf)
+ }
Q <- object$Q
if (is.null(subs) & optQ) subs <- c(seq_len(length(Q) - 1), 0)
bf <- object$bf
@@ -2187,7 +2191,7 @@ optim.pml <- function(object, optNni = FALSE, optBf = FALSE, optQ = FALSE,
CODON = df + (k > 1) + (optInv | (inv > 0)) + freq_df + (dnds != 1) +
(tstv != 1),
USER = df + (k > 1) + (optInv | (inv > 0)) + length(unique(bf)) - 1 + dfQ)
-
+ names(bf) <- NULL
object <- list(logLik = tmp$loglik, inv = inv, k = k, shape = shape,
Q = Q, bf = bf, rate = rate, siteLik = tmp$siteLik,
weight = attr(data, "weight"),
@@ -2201,7 +2205,6 @@ optim.pml <- function(object, optNni = FALSE, optBf = FALSE, optQ = FALSE,
object$frequencies <- bf_choice
}
class(object) <- "pml"
-
extras <- pairlist(bf = bf, Q = Q, inv = inv, shape = shape, rate = rate,
model=model)[c(optBf, optQ, optInv, optGamma, optRate, optModel)]
if (length(extras)) {
@@ -2248,7 +2251,7 @@ optim.pml <- function(object, optNni = FALSE, optBf = FALSE, optQ = FALSE,
updateRates(res, ll, rate, shape, k, inv, wMix, update="rate",
site.rate=site.rate)
}
- if (optBf) {
+ if (optBf && control$statefreq=="estimated") {
if (type=="CODON" && bf_choice=="F3x4") res <- optimF3x4(tree, data,
bf_codon = bf_codon, inv = inv, Q = Q, w = w, g = g, INV = INV,
rate = rate, k = k, llMix = llMix, ASC=ASC)
diff --git a/R/pml_control.R b/R/pml_control.R
index 2197e6e..24c3b21 100644
--- a/R/pml_control.R
+++ b/R/pml_control.R
@@ -19,7 +19,8 @@
#' @param tau minimal edge length.
#' @param minit Minimum number of iterations.
#' @param iter Number of iterations to stop if there is no change.
-#' @param prop Only used if \code{rearrangement=stochstic}. How many NNI moves
+#' @param statefreq take "empirical" or "estimate" state frequencies.
+#' @param prop Only used if \code{rearrangement=stochastic}. How many NNI moves
#' should be added to the tree in proportion of the number of taxa.´
#' @param rell logical, if TRUE approximate bootstraping similar Minh et al.
#' (2013) is performed.
@@ -35,14 +36,17 @@
#' pml.control()
#' pml.control(maxit=25)
#' @export
-pml.control <- function(epsilon = 1e-08, maxit = 10, trace = 1, tau = 1e-8) {
+pml.control <- function(epsilon = 1e-08, maxit = 10, trace = 1, tau = 1e-8,
+ statefreq="empirical") {
if (!is.numeric(epsilon) || epsilon <= 0)
stop("value of 'epsilon' must be > 0")
if (!is.numeric(maxit) || maxit <= 0)
stop("maximum number of iterations must be > 0")
if (!is.numeric(tau) || tau <= 0)
stop("tau must be > 0")
- list(epsilon = epsilon, maxit = maxit, trace = trace, tau = tau)
+ statefreq <- match.arg(statefreq, c("empirical", "estimated"))
+ list(epsilon = epsilon, maxit = maxit, trace = trace, tau = tau,
+ statefreq=statefreq)
}
#' @rdname pml.control
diff --git a/R/treeRearrangement.R b/R/treeRearrangement.R
index bd9e6fd..5f08933 100644
--- a/R/treeRearrangement.R
+++ b/R/treeRearrangement.R
@@ -31,39 +31,6 @@ nnin <- function(tree, n) {
}
-# roughly 2* fast from Martin Smith
-one_nnin <- function(tree, n) {
- edge <- tree$edge
- parent <- edge[, 1]
- child <- edge[, 2]
- lengths <- tree$edge.length
- nb.tip <- length(tree$tip.label)
- ind <- which(child > nb.tip)[n]
- if (is.na(ind)) return(NULL)
- nb.node <- tree$Nnode
- if (nb.node == 1) return(tree)
- p1 <- parent[ind]
- p2 <- child[ind]
- ind1 <- which(parent == p1)
- ind1 <- ind1[ind1 != ind][1]
- ind2 <- which(parent == p2)[sample(2, 1)]
- new_ind <- c(ind2, ind1)
- old_ind <- c(ind1, ind2)
- child_swap <- child[new_ind]
- edge [old_ind, 2L] <- child_swap
- child[old_ind] <- child_swap
-
- neworder <- reorderRcpp(edge, as.integer(nb.tip), as.integer(nb.tip + 1L), 2L)
- tree$edge <- edge[neworder, ]
- if (!is.null(tree$edge.length)) {
- lengths[old_ind] <- lengths[new_ind]
- tree$edge.length <- lengths[neworder]
- }
- attr(tree, "order") <- "postorder"
- tree
-}
-
-
## @aliases nni rNNI rSPR
#' Tree rearrangements.
#'
@@ -268,123 +235,3 @@ oneOf4 <- function(tree, ind1, ind2, from = 1, to = 1, root) {
tree
}
-
-# faster than kSPR
-rSPR_Old <- function(tree, moves = 1, n = 1) {
- k <- length(tree$edge[, 1])
- if (n == 1) {
- trees <- tree
- for (i in 1:moves) trees <- sprMove(trees, sample(k, 1))
- }
- else {
- trees <- vector("list", n)
- for (j in 1:n) {
- tmp <- tree
- for (i in 1:moves) tmp <- sprMove(tmp, sample(k, 1))
- tmp$tip.label <- NULL
- trees[[j]] <- tmp
- }
- attr(trees, "TipLabel") <- tree$tip.label
- class(trees) <- "multiPhylo"
- }
- trees
-}
-
-
-sprMove <- function(tree, m) {
- if (is.rooted(tree)) tree <- unroot(tree)
- if (!is.binary(tree)) stop("Sorry, trees must be binary!")
-
- changeEdge <- function(tree, new, old) {
- tree$edge[tree$edge == old] <- 0L
- tree$edge[tree$edge == new] <- old
- tree$edge[tree$edge == 0L] <- new
- # needed for unrooted trees
- tree <- collapse.singles(tree)
- tree
- }
-
- edge <- tree$edge
- k <- max(edge)
- nTips <- length(tree$tip.label)
- nEdges <- 2 * nTips - 3
- if (m > nEdges) stop("m is chosen too big")
-
- parent <- edge[, 1]
- child <- edge[, 2]
- pv <- integer(k)
- pv[child] <- parent
- cv <- list()
- for (i in unique(parent)) cv[[i]] <- child[parent == i]
- bp <- bip(tree)
- root <- parent[!match(parent, child, 0)][1]
-
- ch <- child[m]
- pa <- parent[m]
-
- candidates <- !logical(k)
- candidates[root] <- FALSE
- candidates[cv[[ch]]] <- FALSE
- candidates[cv[[pa]]] <- FALSE
- candidates[pv[pa]] <- FALSE
- candidates[pa] <- FALSE
-
- ind <- which(candidates)
- l <- sample(ind, 1)
-
- cr <- FALSE
-
- if (!any(is.na(match(bp[[l]], bp[[ch]])))) {
-
- newroot <- cv[[ch]] # [ 1]
- newroot <- newroot[newroot > nTips][1]
- tree <- reroot(tree, newroot, FALSE)
- edge <- tree$edge
- parent <- tree$edge[, 1]
- child <- tree$edge[, 2]
- pv <- integer(k)
- pv[child] <- parent
- cv <- list()
- for (i in unique(parent)) cv[[i]] <- child[parent == i]
-
- tmp <- pa
- pa <- ch
- ch <- tmp
- cr <- TRUE
- }
-
- if (pa == root) {
- cp <- cv[[pa]]
- newroot <- cp[cp != ch]
-
- newroot <- newroot[newroot > nTips][1]
- tree <- reroot(tree, newroot, FALSE)
- edge <- tree$edge
- parent <- tree$edge[, 1]
- child <- tree$edge[, 2]
- pv <- integer(k)
- pv[child] <- parent
- cv <- list()
- for (i in unique(parent)) cv[[i]] <- child[parent == i]
-
- cr <- TRUE
- }
-
- el <- tree$edge.length
- cp <- cv[[pa]]
- sib <- cp[cp != ch]
-
- edge[child == l, 1] <- pa
- edge[child == pa, 1] <- pv[l]
- edge[child == sib, 1] <- pv[pa]
-
- el[child == sib] <- el[child == sib] + el[child == pa]
- el[child == l] <- el[child == l] / 2
- el[child == pa] <- el[child == l]
-
- tree$edge <- edge
- tree$edge.length <- el
- if (cr) tree <- changeEdge(tree, root, newroot)
- tree <- reorder(tree, "postorder")
- tree
-}
diff --git a/TODO b/TODO
deleted file mode 100644
index e6159b8..0000000
--- a/TODO
+++ /dev/null
@@ -1,87 +0,0 @@
-check df for tipdated models
-
-check add_edge_length for tipdated data
-
-rate model:
- * fix df
- * fix print method, guess model
-
-test plotBS speziel bei rooted trees
-
-read.phyDat type="USER" , levels=... DONE
-
-rell bootstrap (Ultrafast Bootstrap) testing TODO
-
-
-summary_edges
-
-vignettes discrete
-
-
-ancestral
-as.phyDat für non-DNA mit [] und contrast für parsimony, export als nexus file
-"array" output
-add nodelabels an results
-
-
-test1 <- ancestral.pars(tree, Laurasiatherian, "ACCTRAN", return = "phyDat")
-test3 <- ancestral.pars(tree, Laurasiatherian, "POSTORDER", return = "phyDat")
-
-# fixed
-test2 <- ancestral.pars(tree, Laurasiatherian, "MPR", return = "phyDat")
-
-
-
-unique.multiPhylo, ratchet + stochastic adding
-mit hash.phylo
-
-
-fix optRooted towards the root!!!
-phylo.R#1547
-
-
-createLabels
-addConfidencesMultiPhylo
-addConfidences.networx
-addConfidences.multiPhylo
-presenceAbsence
-coords
-
-
-matchEdges raus?? in bootstrap.R
-
-
-checks:
- parsimony: RI, CI document
- old2new raus???
-
- phylo: optimF3x4
- init_rell, rell
-
- networx: addConfidencesMultiPhylo, createLabel, addConfidences.multiPhylo,
- presenceAbsence
- coords
- closest.edge, closest.node
-
- phyDat: removeUndeterminedSites
-
- ancestral_pml: ancestral.pars mit allen Varianten
-
- splitsNetwork
-
- treeManipulation: midpoint.multiPhylo, pruneTree
-
- read.phyDat mit nexus format
-
- bug fixes:
-
- fitch.spr both sides und openMP
-
- pmlMix
-
-
-
-optim.pml mit empirical statt estimated als default.
-check my gamma distributions
-
-
diff --git a/debian/changelog b/debian/changelog
index 1bd33e5..439e47c 100644
--- a/debian/changelog
+++ b/debian/changelog
@@ -1,3 +1,9 @@
+r-cran-phangorn (2.11.1+git20230125.1.bac1456+dfsg-1) UNRELEASED; urgency=low
+
+ * New upstream snapshot.
+
+ -- Debian Janitor <janitor@jelmer.uk> Fri, 27 Jan 2023 09:19:22 -0000
+
r-cran-phangorn (2.11.1+dfsg-1) unstable; urgency=medium
* New upstream version
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+
+
+
+<h1 class="title toc-ignore">Markov models and transition rate
+matrices</h1>
+<h4 class="author">Klaus Schliep, Iris Bardel-Kahr</h4>
+<address class="author_afil">
+Graz University of Technology, University of
+Graz<br><a class="author_email" href="mailto:#"><a href="mailto:klaus.schliep@gmail.com" class="email">klaus.schliep@gmail.com</a></a>
+</address>
+<h4 class="date">2023-01-27</h4>
+
+
+
+<div id="introduction" class="section level1">
+<h1>Introduction</h1>
+<p>This document illustrates some of the <em>phangorn</em> <span class="citation">(Schliep 2011)</span> specialized features which are
+useful but maybe not as well-known or just not (yet) described
+elsewhere. This is mainly interesting for someone who wants to explore
+different models or set up some simulation studies. We show how to
+construct data objects for different character states other than
+nucleotides or amino acids or how to set up different models to estimate
+transition rate.</p>
+<p>The vignettes <em>Estimating phylogenetic trees with phangorn</em>
+and <em>Phylogenetic trees from morphological data</em> describe in
+detail how to estimate phylogenies from nucleotides, amino acids or
+morphological data.</p>
+</div>
+<div id="user-defined-data-formats" class="section level1">
+<h1>User defined data formats</h1>
+<p>To better understand how to define our own data type it is useful to
+know a bit more about the internal representation of <code>phyDat</code>
+objects. The internal representation of <code>phyDat</code> object is
+very similar to <code>factor</code> objects.</p>
+<p>As an example we will show here several possibilities to define
+nucleotide data with gaps defined as a fifth state. Ignoring gaps or
+coding them as ambiguous sites - as it is done in most programs, also in
+phangorn as default - may be misleading (see <span class="citation">(Warnow 2012)</span>). When the number of gaps is low
+and randomly distributed, coding gaps as separate state may be not
+important.</p>
+<p>Let’s assume we have given a matrix where each row contains a
+character vector of a taxonomic unit:</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>(phangorn)</span></code></pre></div>
+<pre><code>## Loading required package: ape</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>data <span class="ot"><-</span> <span class="fu">matrix</span>(<span class="fu">c</span>(<span class="st">"r"</span>,<span class="st">"a"</span>,<span class="st">"y"</span>,<span class="st">"g"</span>,<span class="st">"g"</span>,<span class="st">"a"</span>,<span class="st">"c"</span>,<span class="st">"-"</span>,<span class="st">"c"</span>,<span class="st">"t"</span>,<span class="st">"c"</span>,<span class="st">"g"</span>,</span>
+<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a> <span class="st">"a"</span>,<span class="st">"a"</span>,<span class="st">"t"</span>,<span class="st">"g"</span>,<span class="st">"g"</span>,<span class="st">"a"</span>,<span class="st">"t"</span>,<span class="st">"-"</span>,<span class="st">"c"</span>,<span class="st">"t"</span>,<span class="st">"c"</span>,<span class="st">"a"</span>,</span>
+<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a> <span class="st">"a"</span>,<span class="st">"a"</span>,<span class="st">"t"</span>,<span class="st">"-"</span>,<span class="st">"g"</span>,<span class="st">"a"</span>,<span class="st">"c"</span>,<span class="st">"c"</span>,<span class="st">"c"</span>,<span class="st">"t"</span>,<span class="st">"?"</span>,<span class="st">"g"</span>),</span>
+<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a> <span class="at">dimnames =</span> <span class="fu">list</span>(<span class="fu">c</span>(<span class="st">"t1"</span>, <span class="st">"t2"</span>, <span class="st">"t3"</span>),<span class="cn">NULL</span>), <span class="at">nrow=</span><span class="dv">3</span>, <span class="at">byrow=</span><span class="cn">TRUE</span>)</span>
+<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a>data</span></code></pre></div>
+<pre><code>## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
+## t1 "r" "a" "y" "g" "g" "a" "c" "-" "c" "t" "c" "g"
+## t2 "a" "a" "t" "g" "g" "a" "t" "-" "c" "t" "c" "a"
+## t3 "a" "a" "t" "-" "g" "a" "c" "c" "c" "t" "?" "g"</code></pre>
+<p>Normally we would transform this matrix into a phyDat object and gaps
+are handled as ambiguous character (like “?”).</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>gapsdata1 <span class="ot"><-</span> <span class="fu">phyDat</span>(data)</span>
+<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>gapsdata1</span></code></pre></div>
+<pre><code>## 3 sequences with 12 character and 11 different site patterns.
+## The states are a c g t</code></pre>
+<p>Now we will define a “USER” defined object and have to supply a
+vector of levels of the character states for the new data – in our case
+the four nucleotide states and the gap. Additionally we can define
+ambiguous states which can be any of the states.</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>gapsdata2 <span class="ot"><-</span> <span class="fu">phyDat</span>(data, <span class="at">type=</span><span class="st">"USER"</span>, <span class="at">levels=</span><span class="fu">c</span>(<span class="st">"a"</span>,<span class="st">"c"</span>,<span class="st">"g"</span>,<span class="st">"t"</span>,<span class="st">"-"</span>),</span>
+<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a> <span class="at">ambiguity =</span> <span class="fu">c</span>(<span class="st">"?"</span>, <span class="st">"n"</span>))</span></code></pre></div>
+<pre><code>## Warning in phyDat.default(data, levels = levels, return.index = return.index, :
+## Found unknown characters (not supplied in levels). Deleted sites with unknown
+## states.</code></pre>
+<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>gapsdata2</span></code></pre></div>
+<pre><code>## 3 sequences with 10 character and 9 different site patterns.
+## The states are a c g t -</code></pre>
+<p>This is not yet what we wanted, as two sites of our alignment - which
+contain the ambiguous characters “r” and “y” - got deleted. To define
+ambiguous characters like “r” and “y” explicitly we have to supply a
+contrast matrix similar to contrasts for factors.</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>contrast <span class="ot"><-</span> <span class="fu">matrix</span>(<span class="at">data =</span> <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,</span>
+<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a> <span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,</span>
+<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a> <span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,</span>
+<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a> <span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,</span>
+<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a> <span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,</span>
+<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a> <span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,</span>
+<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a> <span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,</span>
+<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a> <span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">0</span>,</span>
+<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a> <span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>),</span>
+<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a> <span class="at">ncol =</span> <span class="dv">5</span>, <span class="at">byrow =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb11-11"><a href="#cb11-11" aria-hidden="true" tabindex="-1"></a><span class="fu">dimnames</span>(contrast) <span class="ot"><-</span> <span class="fu">list</span>(<span class="fu">c</span>(<span class="st">"a"</span>,<span class="st">"c"</span>,<span class="st">"g"</span>,<span class="st">"t"</span>,<span class="st">"r"</span>,<span class="st">"y"</span>,<span class="st">"-"</span>,<span class="st">"n"</span>,<span class="st">"?"</span>),</span>
+<span id="cb11-12"><a href="#cb11-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">c</span>(<span class="st">"a"</span>, <span class="st">"c"</span>, <span class="st">"g"</span>, <span class="st">"t"</span>, <span class="st">"-"</span>))</span>
+<span id="cb11-13"><a href="#cb11-13" aria-hidden="true" tabindex="-1"></a>contrast</span></code></pre></div>
+<pre><code>## a c g t -
+## a 1 0 0 0 0
+## c 0 1 0 0 0
+## g 0 0 1 0 0
+## t 0 0 0 1 0
+## r 1 0 1 0 0
+## y 0 1 0 1 0
+## - 0 0 0 0 1
+## n 1 1 1 1 0
+## ? 1 1 1 1 1</code></pre>
+<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>gapsdata3 <span class="ot"><-</span> <span class="fu">phyDat</span>(data, <span class="at">type=</span><span class="st">"USER"</span>, <span class="at">contrast=</span>contrast)</span>
+<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>gapsdata3</span></code></pre></div>
+<pre><code>## 3 sequences with 12 character and 11 different site patterns.
+## The states are a c g t -</code></pre>
+<p>Here we defined “n” as a state which can be any nucleotide, but not a
+gap. “-” and “?” can be any state including a gap.</p>
+<p>These data can be used in all functions available in
+<em>phangorn</em> to compute distance matrices or perform parsimony and
+maximum likelihood analysis.</p>
+</div>
+<div id="markov-models-of-character-evolution" class="section level1">
+<h1>Markov models of character evolution</h1>
+<p>To model nucleotide substitutions across the edges of a tree T we can
+assign a transition matrix. In the case of nucleotides, with four
+character states, each 4 <span class="math inline">\(\times\)</span> 4
+transition matrix has, at most, 12 free parameters.</p>
+<p>Time-reversible Markov models are used to describe how characters
+change over time, and use fewer parameters. Time-reversible means that
+these models need not be directed in time, and the Markov property
+states that these models depend only on the current state. These models
+are used in analyses of phylogenies using maximum likelihood and MCMC,
+computing pairwise distances, as well as in simulating sequence
+evolution.</p>
+<p>We will now describe the General Time-Reversible (GTR) model <span class="citation">(Tavaré 1986)</span>. The parameters of the GTR model
+are the equilibrium frequencies <span class="math inline">\(\pi = (\pi_A
+,\pi_C ,\pi_G ,\pi_T)\)</span> and a rate matrix <span class="math inline">\(Q\)</span> which has the form <span class="math display">\[\begin{equation}
+Q =
+\begin{pmatrix}
+\ast & \alpha\pi_C & \beta\pi_G & \gamma\pi_T \\
+\alpha\pi_A & \ast & \delta\pi_G & \epsilon\pi_T \\
+\beta\pi_A & \delta\pi_C & \ast & \eta\pi_T \\
+\gamma\pi_A & \epsilon\pi_C & \eta\pi_G & \ast \\
+\end{pmatrix}
+(1)
+\end{equation}\]</span></p>
+<p>where we need to assign 6 parameters <span class="math inline">\(\alpha, \dots, \eta\)</span>. The elements on the
+diagonal are chosen so that the rows sum to zero. The Jukes-Cantor (JC)
+<span class="citation">(Jukes and Cantor 1969)</span> model can be
+derived as special case from the GTR model, for equal equilibrium
+frequencies <span class="math inline">\(\pi_A = \pi_C = \pi_G = \pi_T =
+0.25\)</span> and equal rates set to <span class="math inline">\(\alpha
+= \beta = \gamma = \delta = \eta\)</span>. Table 2 lists all built-in
+nucleotide models in <em>phangorn</em>. The transition probabilities,
+which describe the probabilities of change from character <span class="math inline">\(i\)</span> to <span class="math inline">\(j\)</span> in time <span class="math inline">\(t\)</span>, are given by the corresponding entries
+of the matrix exponential <span class="math display">\[
+P(t) = (p_{ij}(t)) = e^{Qt}, \qquad \sum_j p_{ij}=1
+\]</span> where <span class="math inline">\(P(t)\)</span> is the
+transition matrix spanning a period of time <span class="math inline">\(t\)</span>.</p>
+</div>
+<div id="estimation-of-non-standard-transition-rate-matrices" class="section level1">
+<h1>Estimation of non-standard transition rate matrices</h1>
+<p>In the section <em>User defined data formats</em> we described how to
+set up user defined data formats. Now we describe how to estimate
+transition matrices with pml.</p>
+<p>Again for nucleotide data the most common models can be called
+directly in the <code>optim.pml</code> function (e.g. “JC69”, “HKY”,
+“GTR” to name a few). Table 2 lists all the available nucleotide models,
+which can estimated directly in <code>optim.pml</code>. For amino acids
+several transition matrices are available (“WAG”, “JTT”, “LG”,
+“Dayhoff”, “cpREV”, “mtmam”, “mtArt”, “MtZoa”, “mtREV24”, “VT”,“RtREV”,
+“HIVw”, “HIVb”, “FLU”, “Blosum62”, “Dayhoff_DCMut” and “JTT-DCMut”) or
+can be estimated with <code>optim.pml</code>. For example <span class="citation">Mathews, Clements, and Beilstein (2010)</span> used
+this function to estimate a phytochrome amino acid transition
+matrix.</p>
+<p>We will now show how to estimate a rate matrix with different
+transition (<span class="math inline">\(\alpha\)</span>) and
+transversion ratio (<span class="math inline">\(\beta\)</span>) and a
+fixed rate to the gap state (<span class="math inline">\(\gamma\)</span>) - a kind of Kimura two-parameter
+model (K81) for nucleotide data with gaps as fifth state (see table
+1).</p>
+<table>
+<caption>Tab 1. Rate matrix with 3 parameters to optimize.</caption>
+<thead>
+<tr class="header">
+<th>a</th>
+<th>c</th>
+<th>g</th>
+<th>t</th>
+<th>-</th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td>a</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr class="even">
+<td>c</td>
+<td><span class="math inline">\(\beta\)</span></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr class="odd">
+<td>g</td>
+<td><span class="math inline">\(\alpha\)</span></td>
+<td><span class="math inline">\(\beta\)</span></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr class="even">
+<td>t</td>
+<td><span class="math inline">\(\beta\)</span></td>
+<td><span class="math inline">\(\alpha\)</span></td>
+<td><span class="math inline">\(\beta\)</span></td>
+<td></td>
+<td></td>
+</tr>
+<tr class="odd">
+<td>-</td>
+<td><span class="math inline">\(\gamma\)</span></td>
+<td><span class="math inline">\(\gamma\)</span></td>
+<td><span class="math inline">\(\gamma\)</span></td>
+<td><span class="math inline">\(\gamma\)</span></td>
+<td></td>
+</tr>
+</tbody>
+</table>
+<p>If we want to fit a non-standard transition rate matrix, we have to
+tell <code>optim.pml</code> which transitions in Q get the same rate.
+The parameter vector subs accepts a vector of consecutive integers and
+at least one element has to be zero (these get the reference rate of 1).
+Negative values indicate that there is no direct transition possible and
+the rate is set to zero.</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="fu">library</span>(ape)</span>
+<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>tree <span class="ot"><-</span> <span class="fu">unroot</span>(<span class="fu">rtree</span>(<span class="dv">3</span>))</span>
+<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>fit <span class="ot"><-</span> <span class="fu">pml</span>(tree, gapsdata3)</span>
+<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>fit <span class="ot"><-</span> <span class="fu">optim.pml</span>(fit, <span class="at">optQ=</span><span class="cn">TRUE</span>, <span class="at">subs=</span><span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">2</span>,<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">2</span>),</span>
+<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a> <span class="at">control=</span><span class="fu">pml.control</span>(<span class="at">trace=</span><span class="dv">0</span>))</span>
+<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a>fit</span></code></pre></div>
+<pre><code>## model: Mk
+## loglikelihood: -33.01
+## unconstrained loglikelihood: -28.43
+##
+## Rate matrix:
+## a c g t -
+## a 0.000e+00 2.584e-06 1.000e+00 2.584e-06 0.6912
+## c 2.584e-06 0.000e+00 2.584e-06 1.000e+00 0.6912
+## g 1.000e+00 2.584e-06 0.000e+00 2.584e-06 0.6912
+## t 2.584e-06 1.000e+00 2.584e-06 0.000e+00 0.6912
+## - 6.912e-01 6.912e-01 6.912e-01 6.912e-01 0.0000
+##
+## Base frequencies:
+## a c g t -
+## 0.2 0.2 0.2 0.2 0.2</code></pre>
+<p>Here are some conventions how the models are estimated:</p>
+<p>If a model is supplied the base frequencies bf and rate matrix Q are
+optimized according to the model (nucleotides) or the adequate rate
+matrix and frequencies are chosen (for amino acids). If optQ=TRUE and
+neither a model nor subs are supplied then a symmetric (optBf=FALSE) or
+reversible model (optBf=TRUE, i.e. the GTR for nucleotides) is
+estimated. This can be slow if the there are many character states,
+e.g. for amino acids. Table 2 shows how parameters are optimized and the
+number of parameters to estimate. The elements of the vector subs
+correspond to <span class="math inline">\(\alpha, \dots, \eta\)</span>
+in equation (1)</p>
+<table>
+<caption>Tab 2. DNA models available in phangorn.</caption>
+<thead>
+<tr class="header">
+<th>model</th>
+<th>optQ</th>
+<th>optBf</th>
+<th>subs</th>
+<th>df</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td>JC</td>
+<td>FALSE</td>
+<td>FALSE</td>
+<td><span class="math inline">\(c(0, 0, 0, 0, 0, 0)\)</span></td>
+<td>0</td>
+</tr>
+<tr class="even">
+<td>F81</td>
+<td>FALSE</td>
+<td>TRUE</td>
+<td><span class="math inline">\(c(0, 0, 0, 0, 0, 0)\)</span></td>
+<td>3</td>
+</tr>
+<tr class="odd">
+<td>K80</td>
+<td>TRUE</td>
+<td>FALSE</td>
+<td><span class="math inline">\(c(0, 1, 0, 0, 1, 0)\)</span></td>
+<td>1</td>
+</tr>
+<tr class="even">
+<td>HKY</td>
+<td>TRUE</td>
+<td>TRUE</td>
+<td><span class="math inline">\(c(0, 1, 0, 0, 1, 0)\)</span></td>
+<td>4</td>
+</tr>
+<tr class="odd">
+<td>TrNe</td>
+<td>TRUE</td>
+<td>FALSE</td>
+<td><span class="math inline">\(c(0, 1, 0, 0, 2, 0)\)</span></td>
+<td>2</td>
+</tr>
+<tr class="even">
+<td>TrN</td>
+<td>TRUE</td>
+<td>TRUE</td>
+<td><span class="math inline">\(c(0, 1, 0, 0, 2, 0)\)</span></td>
+<td>5</td>
+</tr>
+<tr class="odd">
+<td>TPM1</td>
+<td>TRUE</td>
+<td>FALSE</td>
+<td><span class="math inline">\(c(0, 1, 2, 2, 1, 0)\)</span></td>
+<td>2</td>
+</tr>
+<tr class="even">
+<td>K81</td>
+<td>TRUE</td>
+<td>FALSE</td>
+<td><span class="math inline">\(c(0, 1, 2, 2, 1, 0)\)</span></td>
+<td>2</td>
+</tr>
+<tr class="odd">
+<td>TPM1u</td>
+<td>TRUE</td>
+<td>TRUE</td>
+<td><span class="math inline">\(c(0, 1, 2, 2, 1, 0)\)</span></td>
+<td>5</td>
+</tr>
+<tr class="even">
+<td>TPM2</td>
+<td>TRUE</td>
+<td>FALSE</td>
+<td><span class="math inline">\(c(1, 2, 1, 0, 2, 0)\)</span></td>
+<td>2</td>
+</tr>
+<tr class="odd">
+<td>TPM2u</td>
+<td>TRUE</td>
+<td>TRUE</td>
+<td><span class="math inline">\(c(1, 2, 1, 0, 2, 0)\)</span></td>
+<td>5</td>
+</tr>
+<tr class="even">
+<td>TPM3</td>
+<td>TRUE</td>
+<td>FALSE</td>
+<td><span class="math inline">\(c(1, 2, 0, 1, 2, 0)\)</span></td>
+<td>2</td>
+</tr>
+<tr class="odd">
+<td>TPM3u</td>
+<td>TRUE</td>
+<td>TRUE</td>
+<td><span class="math inline">\(c(1, 2, 0, 1, 2, 0)\)</span></td>
+<td>5</td>
+</tr>
+<tr class="even">
+<td>TIM1e</td>
+<td>TRUE</td>
+<td>FALSE</td>
+<td><span class="math inline">\(c(0, 1, 2, 2, 3, 0)\)</span></td>
+<td>3</td>
+</tr>
+<tr class="odd">
+<td>TIM1</td>
+<td>TRUE</td>
+<td>TRUE</td>
+<td><span class="math inline">\(c(0, 1, 2, 2, 3, 0)\)</span></td>
+<td>6</td>
+</tr>
+<tr class="even">
+<td>TIM2e</td>
+<td>TRUE</td>
+<td>FALSE</td>
+<td><span class="math inline">\(c(1, 2, 1, 0, 3, 0)\)</span></td>
+<td>3</td>
+</tr>
+<tr class="odd">
+<td>TIM2</td>
+<td>TRUE</td>
+<td>TRUE</td>
+<td><span class="math inline">\(c(1, 2, 1, 0, 3, 0)\)</span></td>
+<td>6</td>
+</tr>
+<tr class="even">
+<td>TIM3e</td>
+<td>TRUE</td>
+<td>FALSE</td>
+<td><span class="math inline">\(c(1, 2, 0, 1, 3, 0)\)</span></td>
+<td>3</td>
+</tr>
+<tr class="odd">
+<td>TIM3</td>
+<td>TRUE</td>
+<td>TRUE</td>
+<td><span class="math inline">\(c(1, 2, 0, 1, 3, 0)\)</span></td>
+<td>6</td>
+</tr>
+<tr class="even">
+<td>TVMe</td>
+<td>TRUE</td>
+<td>FALSE</td>
+<td><span class="math inline">\(c(1, 2, 3, 4, 2, 0)\)</span></td>
+<td>4</td>
+</tr>
+<tr class="odd">
+<td>TVM</td>
+<td>TRUE</td>
+<td>TRUE</td>
+<td><span class="math inline">\(c(1, 2, 3, 4, 2, 0)\)</span></td>
+<td>7</td>
+</tr>
+<tr class="even">
+<td>SYM</td>
+<td>TRUE</td>
+<td>FALSE</td>
+<td><span class="math inline">\(c(1, 2, 3, 4, 5, 0)\)</span></td>
+<td>5</td>
+</tr>
+<tr class="odd">
+<td>GTR</td>
+<td>TRUE</td>
+<td>TRUE</td>
+<td><span class="math inline">\(c(1, 2, 3, 4, 5, 0)\)</span></td>
+<td>8</td>
+</tr>
+</tbody>
+</table>
+<div id="predefined-models-for-user-defined-data" class="section level2">
+<h2>Predefined models for user defined data</h2>
+<p>So far there are 4 models which are just a generalization from
+nucleotide models allowing different number of states. In many cases
+only the equal rates (ER) model will be appropriate.</p>
+<table>
+<caption>Tab 2: Build in models which are available for USER defined
+data.</caption>
+<thead>
+<tr class="header">
+<th>DNA</th>
+<th>USER</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td>JC</td>
+<td>ER</td>
+</tr>
+<tr class="even">
+<td>F81</td>
+<td>FREQ</td>
+</tr>
+<tr class="odd">
+<td>SYM</td>
+<td>SYM</td>
+</tr>
+<tr class="even">
+<td>GTR</td>
+<td>GTR</td>
+</tr>
+</tbody>
+</table>
+<p>There is an additional model ORDERED, which assumes ordered
+characters and only allows to switch between neighboring states. Table 3
+show the corresponding rate matrix.</p>
+<table>
+<caption>Tab 3: Rate matrix for “ORDERED” model with 5 states.</caption>
+<thead>
+<tr class="header">
+<th>a</th>
+<th>b</th>
+<th>c</th>
+<th>d</th>
+<th>e</th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td>a</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr class="even">
+<td>b</td>
+<td>1</td>
+<td></td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr class="odd">
+<td>c</td>
+<td>0</td>
+<td>1</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr class="even">
+<td>d</td>
+<td>0</td>
+<td>0</td>
+<td>1</td>
+<td></td>
+<td></td>
+</tr>
+<tr class="odd">
+<td>e</td>
+<td>0</td>
+<td>0</td>
+<td>0</td>
+<td>1</td>
+<td></td>
+</tr>
+</tbody>
+</table>
+</div>
+</div>
+<div id="codon-substitution-models" class="section level1">
+<h1>Codon substitution models</h1>
+<p>A special case of the transition rates are codon models.
+<em>phangorn</em> now offers the possibility to estimate the <span class="math inline">\(d_N/d_S\)</span> ratio (sometimes called ka/ks),
+for an overview see <span class="citation">(Yang 2014)</span>. These
+functions extend the option to estimate the <span class="math inline">\(d_N/d_S\)</span> ratio for pairwise sequence
+comparison as it is available through the function <code>kaks</code> in
+<em>seqinr</em>. The transition rate between between codon <span class="math inline">\(i\)</span> and <span class="math inline">\(j\)</span> is defined as follows: <span class="math display">\[\begin{eqnarray}
+q_{ij}=\left\{
+ \begin{array}{l@{\quad}l}
+ 0 & \textrm{if i and j differ in more than one position} \\
+ \pi_j & \textrm{for synonymous transversion} \\
+ \pi_j\kappa & \textrm{for synonymous transition} \\
+ \pi_j\omega & \textrm{for non-synonymous transversion} \\
+ \pi_j\omega\kappa & \textrm{for non-synonymous transition}
+ \end{array}
+ \right. \nonumber
+\end{eqnarray}\]</span></p>
+<p>where <span class="math inline">\(\omega\)</span> is the <span class="math inline">\(d_N/d_S\)</span> ratio, <span class="math inline">\(\kappa\)</span> the transition transversion ratio
+and <span class="math inline">\(\pi_j\)</span> is the the equilibrium
+frequency of codon <span class="math inline">\(j\)</span>. For <span class="math inline">\(\omega\sim1\)</span> the amino acid change is
+neutral, for <span class="math inline">\(\omega < 1\)</span>
+purifying selection and <span class="math inline">\(\omega >
+1\)</span> positive selection.</p>
+<p>Here we use a data set from and follow loosely the example in <span class="citation">Bielawski and Yang (2005)</span>. We first read in an
+alignment and phylogenetic tree for 45 sequences of the nef gene in the
+Human HIV-2 Genome using <code>read.phyDat</code> function.</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>fdir <span class="ot"><-</span> <span class="fu">system.file</span>(<span class="st">"extdata/trees"</span>, <span class="at">package =</span> <span class="st">"phangorn"</span>)</span>
+<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>hiv_2_nef <span class="ot"><-</span> <span class="fu">read.phyDat</span>(<span class="fu">file.path</span>(fdir, <span class="st">"seqfile.txt"</span>), <span class="at">format=</span><span class="st">"sequential"</span>)</span>
+<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>tree <span class="ot"><-</span> <span class="fu">read.tree</span>(<span class="fu">file.path</span>(fdir, <span class="st">"tree.txt"</span>))</span></code></pre></div>
+<p>With the tree and data set we can estimate currently 3 different site
+models:</p>
+<ol style="list-style-type: decimal">
+<li>The M0 model with a constant <span class="math inline">\(\omega\)</span>, where <span class="math inline">\(\omega\)</span> estimates the average over all
+sites of the alignment. M0 does not allow for distinct <span class="math inline">\(\omega\)</span> and identifies classes, therefore
+we will not retrieve any information regarding positive selection.<br />
+</li>
+<li>The M1a or nearly neutral model estimates two different <span class="math inline">\(\omega\)</span> value classes (<span class="math inline">\(\omega=1\)</span> & <span class="math inline">\(\omega<1\)</span>).</li>
+<li>The M2a or positive selection model estimates three different
+classes of <span class="math inline">\(\omega\)</span> (negative
+selection <span class="math inline">\(\omega<1\)</span>, neutral
+selection <span class="math inline">\(\omega=1\)</span>, positive
+selection <span class="math inline">\(\omega>1\)</span>). One can use
+a likelihood ratio test to compare the M1a and M2a to for positive
+selection.</li>
+</ol>
+<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>cdn <span class="ot"><-</span> <span class="fu">codonTest</span>(tree, hiv_2_nef)</span>
+<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>cdn</span></code></pre></div>
+<pre><code>## model Frequencies estimate logLik df AIC BIC dnds_0 dnds_1 dnds_2
+## 1 M0 F3x4 empirical -9773 98 19741 20087 0.50486 NA NA
+## 2 M1a F3x4 empirical -9313 99 18824 19168 0.06281 1 NA
+## 3 M2a F3x4 empirical -9244 101 18689 19040 0.05551 1 2.469
+## p_0 p_1 p_2 tstv
+## 1 1.0000 NA NA 4.418
+## 2 0.5563 0.4437 NA 4.364
+## 3 0.5227 0.3617 0.1156 4.849</code></pre>
+<p>Currently the choice of site models is limited to the three models
+mentioned above and are no branch models implemented so far.</p>
+<p>We can identify sites under positive selection using the Na"ive
+empirical Bayes (NEB) method of <span class="citation">Yang and Nielsen
+(1998)</span>: <span class="math display">\[
+P(\omega|x_h) = \frac{P(X_h|\omega_i)p_i}{P(X_h)} =
+\frac{P(X_h|\omega_i)p_i}{\sum_j P(X_h|\omega_j)p_j}
+\]</span></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">plot</span>(cdn, <span class="st">"M1a"</span>)</span></code></pre></div>
+<p><img 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" /><!-- --></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">plot</span>(cdn, <span class="st">"M2a"</span>)</span></code></pre></div>
+<p><img 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" /><!-- --></p>
+<p>A lot if implementations differ in the way the codon frequencies are
+derived. The M0 model can be also estimated using <code>pml</code> and
+<code>optim.pml</code> functions. There are several ways to estimate the
+codon frequencies <span class="math inline">\(\pi_j\)</span>. The
+simplest model is to assume they have equal frequencies (=1/61). A
+second is to use the empirical codon frequencies, either computed using
+<code>baseFreq</code> or using the argument <code>bf="empirical"</code>
+in <code>pml</code>. This is usually not really good as some codons are
+rare and have a high variance. One can estimate the frequencies from
+nucleotide frequencies with the F1x4 model. Last but not least the
+frequencies can be derived from the base frequencies at each codon
+position, the F3x4 model is set by the argument
+<code>bf="F3x4"</code>.</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>treeM0 <span class="ot"><-</span> cdn<span class="sc">$</span>estimates[[<span class="st">"M0"</span>]]<span class="sc">$</span>tree <span class="co"># tree with edge lengths</span></span>
+<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>M0 <span class="ot"><-</span> <span class="fu">pml</span>(treeM0, <span class="fu">dna2codon</span>(hiv_2_nef), <span class="at">bf=</span><span class="st">"F3x4"</span>)</span>
+<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a>M0 <span class="ot"><-</span> <span class="fu">optim.pml</span>(M0, <span class="at">model=</span><span class="st">"codon1"</span>, <span class="at">control=</span><span class="fu">pml.control</span>(<span class="at">trace=</span><span class="dv">0</span>))</span>
+<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a>M0</span></code></pre></div>
+<pre><code>## model: codon1
+## loglikelihood: -9773
+## unconstrained loglikelihood: -1372
+## dn/ds: 0.5049
+## ts/tv: 4.418
+## Freq: F3x4</code></pre>
+<p>For the F3x4 model can optimize the codon frequencies setting the
+option to <code>optBf=TRUE</code> in <code>optim.pml</code>.</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>M0_opt <span class="ot"><-</span> <span class="fu">optim.pml</span>(M0, <span class="at">model=</span><span class="st">"codon1"</span>, <span class="at">optBf=</span><span class="cn">TRUE</span>, <span class="at">control=</span><span class="fu">pml.control</span>(<span class="at">trace=</span><span class="dv">0</span>))</span>
+<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>M0_opt</span></code></pre></div>
+<pre><code>## model: codon1
+## loglikelihood: -9668
+## unconstrained loglikelihood: -1372
+## dn/ds: 0.51
+## ts/tv: 4.581
+## Freq: F3x4</code></pre>
+</div>
+<div id="session-info" class="section level1">
+<h1>Session info</h1>
+<pre><code>## R version 4.2.2 Patched (2022-11-10 r83330)
+## Platform: x86_64-pc-linux-gnu (64-bit)
+## Running under: Debian GNU/Linux bookworm/sid
+##
+## Matrix products: default
+## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0
+## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
+##
+## locale:
+## [1] C
+##
+## attached base packages:
+## [1] stats graphics grDevices utils datasets methods base
+##
+## other attached packages:
+## [1] phangorn_2.12.0.900 ape_5.6-2
+##
+## loaded via a namespace (and not attached):
+## [1] igraph_1.3.5 Rcpp_1.0.10 knitr_1.41 magrittr_2.0.3
+## [5] lattice_0.20-45 R6_2.5.1 quadprog_1.5-8 rlang_1.0.6
+## [9] fastmatch_1.1-3 fastmap_1.1.0 highr_0.10 stringr_1.5.0
+## [13] tools_4.2.2 parallel_4.2.2 grid_4.2.2 nlme_3.1-161
+## [17] xfun_0.36 cli_3.6.0 jquerylib_0.1.4 htmltools_0.5.4
+## [21] yaml_2.3.6 digest_0.6.31 lifecycle_1.0.3 Matrix_1.5-3
+## [25] codetools_0.2-18 sass_0.4.4 vctrs_0.5.1 glue_1.6.2
+## [29] cachem_1.0.6 evaluate_0.20 rmarkdown_2.20 stringi_1.7.12
+## [33] compiler_4.2.2 bslib_0.4.2 generics_0.1.3 jsonlite_1.8.4
+## [37] pkgconfig_2.0.3</code></pre>
+</div>
+<div id="references" class="section level1 unnumbered">
+<h1 class="unnumbered">References</h1>
+<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="ref-Bielawski2005" class="csl-entry">
+Bielawski, Joseph P., and Ziheng Yang. 2005. <span>“Maximum Likelihood
+Methods for Detecting Adaptive Protein Evolution.”</span> In
+<em>Statistical Methods in Molecular Evolution</em>, 103–24. New York,
+NY: Springer New York. <a href="https://doi.org/10.1007/0-387-27733-1_5">https://doi.org/10.1007/0-387-27733-1_5</a>.
+</div>
+<div id="ref-Jukes1969" class="csl-entry">
+Jukes, Thomas H., and Charles R. Cantor. 1969. <span>“{CHAPTER} 24 -
+Evolution of Protein Molecules.”</span> In <em>Mammalian Protein
+Metabolism</em>, edited by H. N. Munro, 21–132. Academic Press.
+</div>
+<div id="ref-Mathews2010" class="csl-entry">
+Mathews, S., M. D. Clements, and M. A. Beilstein. 2010. <span>“A
+Duplicate Gene Rooting of Seed Plants and the Phylogenetic Position of
+Flowering Plants.”</span> <em>Phil. Trans. R. Soc. B</em> 365: 383–95.
+</div>
+<div id="ref-Schliep2011" class="csl-entry">
+Schliep, Klaus Peter. 2011. <span>“Phangorn: Phylogenetic Analysis in
+<span>R</span>.”</span> <em>Bioinformatics</em> 27 (4): 592–93. <a href="https://doi.org/10.1093/bioinformatics/btq706">https://doi.org/10.1093/bioinformatics/btq706</a>.
+</div>
+<div id="ref-Tavare1986" class="csl-entry">
+Tavaré, Simon. 1986. <span>“Some Probabilistic and Statistical Problems
+in the Analysis of DNA Sequences.”</span> <em>Lectures on Mathematics in
+the Life Sciences</em>, no. 17: 57–86.
+</div>
+<div id="ref-Warnow2012" class="csl-entry">
+Warnow, Tandy. 2012. <span>“Standard Maximum Likelihood Analyses of
+Alignments with Gaps Can Be Statistically Inconsistent.”</span> <em>PLOS
+Currents Tree of Life</em>.
+</div>
+<div id="ref-Yang2014" class="csl-entry">
+Yang, Ziheng. 2014. <em>Molecular Evolution: A Statistical
+Approach</em>. Oxford: Oxford University Press.
+</div>
+<div id="ref-Yang1998" class="csl-entry">
+Yang, Ziheng, and Rasmus Nielsen. 1998. <span>“Synonymous and
+Nonsynonymous Rate Variation in Nuclear Genes of Mammals.”</span>
+<em>Journal of Molecular Evolution</em> 46 (4): 409–18. <a href="https://doi.org/10.1007/PL00006320">https://doi.org/10.1007/PL00006320</a>.
+</div>
+</div>
+</div>
+
+
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+
+
+
+<h1 class="title toc-ignore">Ancestral Sequence Reconstruction</h1>
+<h4 class="author">Klaus Schliep</h4>
+<address class="author_afil">
+Graz University of
+Technology<br><a class="author_email" href="mailto:#"><a href="mailto:klaus.schliep@gmail.com" class="email">klaus.schliep@gmail.com</a></a>
+</address>
+<h4 class="date">2023-01-27</h4>
+
+
+
+<div id="introduction" class="section level1">
+<h1>Introduction</h1>
+<p>These notes describe the ancestral sequence reconstruction using the
+<em>phangorn</em> package <span class="citation">(Schliep 2011)</span>.
+<em>phangorn</em> provides several methods to estimate ancestral
+character states with either Maximum Parsimony (MP) or Maximum
+Likelihood (ML). For more background on all the methods see e.g. <span class="citation">(Felsenstein 2004)</span> or <span class="citation">(Yang 2006)</span>.</p>
+</div>
+<div id="parsimony-reconstructions" class="section level1">
+<h1>Parsimony reconstructions</h1>
+<p>To reconstruct ancestral sequences we first load some data and
+reconstruct a tree:</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>(phangorn)</span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>fdir <span class="ot"><-</span> <span class="fu">system.file</span>(<span class="st">"extdata/trees"</span>, <span class="at">package =</span> <span class="st">"phangorn"</span>)</span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>primates <span class="ot"><-</span> <span class="fu">read.phyDat</span>(<span class="fu">file.path</span>(fdir, <span class="st">"primates.dna"</span>),</span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a> <span class="at">format =</span> <span class="st">"interleaved"</span>)</span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>tree <span class="ot"><-</span> <span class="fu">pratchet</span>(primates, <span class="at">trace=</span><span class="dv">0</span>) <span class="sc">|></span> <span class="fu">acctran</span>(primates)</span>
+<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="fu">parsimony</span>(tree, primates)</span></code></pre></div>
+<pre><code>## [1] 746</code></pre>
+<p>For parsimony analysis of the edge length represent the observed
+number of changes. Reconstructing ancestral states therefore defines
+also the edge lengths of a tree. However there can exist several equally
+parsimonious reconstructions or states can be ambiguous and therefore
+edge length can differ. “MPR” reconstructs the ancestral states for each
+(internal) node as if the tree would be rooted in that node. However the
+nodes are not independent of each other. If one chooses one state for a
+specific node, this can restrict the choice of neighboring nodes
+(figures 2 and 3). The function acctran (accelerated transformation)
+assigns edge length and internal nodes to the tree <span class="citation">(Swofford and Maddison 1987)</span>.</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>anc.acctran <span class="ot"><-</span> <span class="fu">ancestral.pars</span>(tree, primates, <span class="st">"ACCTRAN"</span>)</span>
+<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>anc.mpr <span class="ot"><-</span> <span class="fu">ancestral.pars</span>(tree, primates, <span class="st">"MPR"</span>)</span></code></pre></div>
+<p>All the ancestral reconstructions for parsimony are based on the
+fitch algorithm and so far only bifurcating trees are allowed. However
+trees can get pruned afterwards using the function <code>multi2di</code>
+from <em>ape</em>.</p>
+<p>The <code>seqLogo</code> function from the <em>seqLogo</em> package
+from Bioconductor provides a neat way to show proportions of a
+nucleotides of ancestral states (see figure 1).</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="fu">library</span>(seqLogo)</span>
+<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="fu">seqLogo</span>( <span class="fu">t</span>(<span class="fu">subset</span>(anc.mpr, <span class="fu">getRoot</span>(tree), <span class="dv">1</span><span class="sc">:</span><span class="dv">20</span>)[[<span class="dv">1</span>]]), <span class="at">ic.scale=</span><span class="cn">FALSE</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAMAAABF6+6qAAAC5VBMVEUAAAAAAP8A/wABAQECAgIDAwMEBAQFBQUHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OVlZWXl5eYmJiampqbm5ucnJydnZ2goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKiqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7/AAD/pQD///8AKt9MAAAgAElEQVR4nO2de6BVVZ3H9zmKzpXERDAQMF93yEmHRJAohbDBwlGwyR6jzkyaPW2SSq1wytIejjrOmOZopokPSkdBQhQkUgcpzXxhBiqhFiY4il6I9fec1z5nPX5r7bXW3uvsfc7+fv+47P09+3zX7/fbn3vO4d5zz44YBAVQlHcBUH8KYEFBBLCgIAJYUBABLCiIABYURAALCiKABQURwIKCCGBBQQSwoCACWFAQASwoiAAWFEQACwoigAUFEcCCgghgQUEEsKAgAlhQEAEsKIgAFhREAAsKIoAFBRHAgoIIYEFBBLCgIAJYUBABLCiIABYURAALCiKABQURwIKCCGBBQQSwoCDKBqy5j2QSA/WPMgFrYfRgFjFQHykDsG4YjAAWJCkDsNYtvgZgQZIyeSp8BmBBkrIH64uzW5qcRTTUowr4iDUri2ioRwWwoCACWFAQASwoiAAWFEQBf1eYAFa1JsKuVKiDSZdMqDq4tVgql3f/QspcgqZcYTFzrLYwwhRKSCiXjHWamLXyBUutvEL2TrupwbJYzAss31yXALqEZLDUBIBFJVjG6hdTcwXXByyLWAuwkhLIEszl6gP6HqwK2TvtFhssuon2ngdYNlMAWBmA5TIQ78UyBsuc6xJAl2ABVqo52gtgybmi6wGWTawNWAkJZAnGcg0BJQGrQpryRAoAlndsKLDaEvBPDuh3sGiEugqWtJg/WHRuvGvxyGI3BXew0szRXiUGy2qxrMEy5vpMQdMwwBKUC1gVk+sOllWsFVh0AtWZFVh0bLnAqiSamoA8wJJyQ4MljQFgyTKdExosaqQ2sRq3t8AyTgFgccobLLfFyBctZrCscv+i5npMwQ2sFHO0V6+DpfQOsHTlAqxug1UhXSkhHVj0YiwBLJvCrMHSllAqsPxHmhlYBAF0rJjrEesIlpgAsCSZv9krpGkzUoClKbfEYNEI+YBlg1tvgeU4BYDFqwBgaRfzAYvOVWP/4haQHiwigW5Nce1VWrAcF8sOLDEXYLmrd8CqkK6Y0OdgyQmaWHuVCSzB7SpY9GJxrB4si8JswdKXUC6wvEeaHVgqARZN+MS6giUkACxJtmA5DsQ0JoAFsDRgWYwUYNHlJgRYTMxNBQKLRqirYOkX8wLL4mHXEizXKQAsTgAruykkgMUnACyrkdqOyXWxDMEScgGWu2zASnOuAZZ2CgCrIGBVSFeISAMWvVg71gBWcmEAS1IPgKUQoGkibWw3wNKXUDawPEdaCrBULpTKAFZHjmC5DARgASyp94p2IgCLLoEut8Rg0QgVGqyMYgGWm3oPLP1inmDRuXJsMgGGwjQEAKy22i1TI5VcMsDJNZ0/YjFHsOgmbMCiETIU5gZWyjk6qIBgsS6AlbAYkxdzA4uRYMluO5YAy7qwqhNYVAklAUvc0rlkgItrOn/UYkaw7JuwBct5CvZgpZqjg0oNVmczabEMwJLX7cQCrI52fGdwzOlbm9uvfG7cyLnr1GO8wbI715mCRW/yEQ5g2a3QiaXBsivMCSyyhGKBdfGe197xjg81t0+d8NPlMw/dphzjA5blJhcg9d7HYDlMQSnXUEKhwBra/3uMrdr1qcb2QG37ieh+5aDCg8VtGh4pW7ICq0KWS7udWIUAh8LcwdLMkW5NjrWXH1iPRY/Wng5H/bC+PfSWSxlbT1xNJyVYft+r7mDJBCglNJUWLGLhOBZgtbUseqX2ddK3GzunH3TjsuPe+aZykBEs07NI4ktgTQLn0quRizkQkNiEX6wGLKsEF7DoEgoF1o3RjtrXo/+1sfPSflE07Jft2z49paX9TAlGsDrbipk0UkvcAJacQLcmx9rLD6y7oi21r5O+Ud/eMnjGc5svfetDykEej1j8tgKW1Uj7FyyXKfQsWA9Hj9e+7vuD+vbCPerPgrPOVg5yA4tGqPtg0Yt5gMWEWBewlDTnKRjBYmIJhQJr+4T/YGzNLo3/Fd60+59rX6d8STmo6GC5LOYLFp0rxRYCLDFBF2svz59jfWefO++fchJjV19Ueyo8dtnqz+7xiHJM8cASV1N2QoJFr6yAldEUksFKSMgNLPbtwTGf+D/GThhkbP0pY/eevVo9pMBgiUnkYhQBKZqgDzGDZUO8A1iaRQoGloVSgOU/UuOYugKW2lG2YDGLKQAsG7CY4tIJehdg6UtQZmNwXVQosJiyB7ASCgNYkooGVsW8mA9YjItND5amMIAlKRBYlmPKESzpGAIscS+hMB+whL1+BUs4152buwCWGNvfYBlKAFjyXjfAkgjQNaG5C8AKo54Bi2nPX2ZgqYtxsVqwLAoDWJK8H0S0AS6uzfkjCNA1kTK2O2BpSigZWPqRagNc3J4GixyDXJoogMXEXeIhvKILSHAZwDIGlA8sprh0QIIbAKysHghzB4slT8xZRQKLvx1g2SXoHlp21pQU2w7oZ7BohABWnMBU19DwzqYSYtsBAMsKLIsxOZ8/L7BYRmDZTEFoeOdOiSy6sHZCv4HFV14AsAyLMTNYTk0kgZUY4ADWTvUe8jIASzoqLVhOi7EMwWLBwdq5UyYrIQFg9TtY/lMAWC1lAhZTyLIbk/NiLGOwOvsBwNq5UyELYLXVVbBYaLDkxQBWGgGs1GAlFkaAtXOnSlZ5wKr2AFjqiyGiCaEEGqFcwBK3DCUALOmoBLCMuPU4WEwCSySAe80OsDy/VwGWCpbwg1GAlT9YlVBg0YvxsSnBYmawduoKk5qgW2O+KjxYTHXbCUUAS8q1iw0MVudIB7D4iRHPsK4qF1hV/WKFBctYGMCSBLC42CzBErlq7ZpKKAFY4hFWYCm9J4ypZ8FisqsrQQKruQ+wOBUDLJEAogm+BCWAFR8sZTYG10kAq21QixUWLI4LGSzh0DKCZXwWAVhWhXmCxcoNltuTQGzq3B4BiyghDVistGB1Top4RGCwLBcLARYLCJb8EstUWDugXGAZXClBMn3ASlqMeYKld/lYA1hJhfmCJQQALDpBMgGWSwn6ibE+BstrIBYuwBIC8gLrNc9ogJXUBAsBFh1QRLDeesbyHT7RhQKLASxtQF5g/ej43d7+tUfdo/sdLOqBkG4iITZvsFjyxNxl9Rrrhf86epcpl23iHP7Svey2mSPeo14HE2DZxmYCVrOGHgOLsY3nDot2O2Fte5+/dO+igUuWzhv9knIfgGUZ6w9WVZ5jb4G1/rJjdj3wnPuXzxn1RsvhL93LJi9g7OU5P1fu5gKW88sTS4Rot4Rg6dbPE6xp0UHnNJ7pXol+07L4S/c+GT1M3y8ZrE7lPQ5WQhMJsWUF69wHWhtDDw61tvhL9y6PbjlyxPR71fuVESyv2ASw7KbQg2BN3Vb/uvF9nMVfundhdND1950+8ER82xdntzTOkJklWFXRZQDLoYTcwHpxwYLovAU1/ePbOJe/dO/t0R2MbR88T7lr8EcsBrB6F6z1s2dHx9Yffz7wY87lL937QPRc7euJpyl37TGwzD/IBFiOsn4qFMRfuve1Ubcw9sYBFyoHGcCqypUDLK/CKLBisrhPbygiWCvXsZUt8TZ36V52wdif/OJj+7yg3DVfsEy4ZQcWA1gaJYEVncWilgSfu3Qv+9bEvecQv/IBWAaXj00NVqe13gFraLtvMsAyLCbElhKspR25RmcClimBAazW0p3WUoBVFczQYO3WkWt0ScASmiDuCLCyVhHBMhBQALB8XxCIYJF/sKpbPzewNP8rtFFRwKoqLsCSE7oPlu5/hRbqBlhS7wCL+FAQE1jqbGTXULtRRf9foSmB/1dwSw6W9DFG6cDyJcvmcehXn5g+8wu/c44GWElNsOzBIj4fq7Bg3bXrpK+cO3nggeQjRSWAxf/bXbCcz19dPQlW4v8KcwVr6qk7GNvxz8e4RluCZT7XNgmCqXfLA5b4GaSFBWtE4018941wjQZYSU2wUGDxH8ddXLDefW3963Xvco0GWElNsABgKRcQKChYW7duvXv/a57dcO3oq12jAVZSEywcWLwswFInJtzqocSfY7U1yjXaESziMJ+BWLj9DZZKViHBerCtX7pGA6yEJuLYUoJV1/aXalq1l2t0kcASNrIBS10MYHGyAGvpyMZT4d+5RgOshCbi2IzBUi8slxyQMDEPWYB11En3Tliy4tDHXaMBVkITcWw2YJEPWUm/hM4VrOHL2SmL2C0nukYDrIQm4lhvsKrKHJvqEbDG3sHO/w57YqRrdD5gsWTXCyxzCV5NWJbgDlbnJ1iFBusj7/7tzUe+8P3xrtEWYDHfcwKw6BJitX9KWmSwfn/Ev2+bMiy62DW6v8BSZpwVWNyt2YFlW0KuYNX16h3EJ6slqGtgVXlX3NAtVhiwKvyyZQMrzPuxhA0fsNobAKs3wQr4fqzkc20T0MNgVSo8WRmCJdypoGBl/36sqv25NiRIUbJLH2tcLAmsKmkUECwxtqhgZf9+rK6ApcctO7DEW9zBqlQEBLIGiwNWU0CuYGX/fqweBKtaaLAkAipqrqaA/MAK8n4sgNVZkifAByx5S4xt5xraUgO6AVaQ92MBrM6SBQKrypnBweq8H+tB1+hCgqUnwBss75+ZBAKrIshcQn5g1fXqlfPPumJr8nGSAFZCExQAZQLr6XEjj/vgqHHOPyHtClhC7xZj6n+wRK6KDNaJc7bUXsQfP881GmAlNBESrE68qQRiNlrXURZgjWr+HGu0azTASmgiIFjiZkHBGn9n/euS/V2jAZa5CfUpy7UwagocV0UH6/TDf8vYY5M+6RptBkvcyuR7tZrgFhos/Yshf7DinYKCtXlmNHZsNGOza7QtWPVNgJUlWPJ9CwpW7fXVlVfe5x4NsJjeZQDr1alPJcdsI64a3YdgVSVH3CoCWNLB1mDJE1Nud5XFI9a8K1VPuHQvYw8PrFaPCQhWEkI9BBa/kT1Yzf2CgrVi+ldvWlwT7/GX7mXs9UlRL4BlJsAIVjV7sDo4CYi5FNbjYA20xFnCpXsZmz+t4GBJm44PLQxgucvvc975S/cydteYNe5gcWJ+YAkBTm4WYKWOrXDdpQBL6lc+1gIsyzk6KhGsHU8/9Lpi8pfuZX8c97NnAZZzbOZg0TUUFawNU6NozArZ5S/dyz58JuPBOju+dK/+L1z7Fyy6CTLSGiwXLtxKyBOsj429fvG08fKlMPlL915zyBYBrNf+1NJMbSjAko/2Aquq1tA7YI35Zh2jX0suf+nezzTfYDpVuavLU6Hjk0DxwHI+q5XugpV+jo5KfGvy3Yy9GcnX0eEv3bth7dq1S6Jr1U85AlimWBUs7U8y+xOslfEXQfyle2tK/eIdYGUGlmYtmwSj6yhPsIRL9+YOVpV0HRcLCJbFyS4fWJNnzZrV+KLnRCOAZWqi7GCd1pFrdD+CVVUdgEUqjyusUlikHqnq0se6LyaEmEoAWJz6GCw9bgCLTjC6jiosWI1hUK4uIQ1Y2sUqcoapCfX8tWKpJuzAMhaWAix6jnRrRKCFygpWRY7tHlg0QwDLWqnB0rn9ABbluhaWOVhVXWceKh1YJAFWYLHQYBlcsjB/sCqmOQKs7MGix+8Nlg9ClmDpS2CqABanrMCSTTcCuIpZIljU66aMwCILs+msk0IZ5QDL8sWsLgFgUZ11UgiDmo3OdRXAaq/ev2DRhQEswSwdWLrZOPz/AWBxIkfq9NqgIGBZ4ib0Z56C6XEbYFmc666CpT9/LBBYxGSyB8tQQjnBqpAjzR+s9pSLCxY9R2qdfgSL3/YDS0lTzrviuoHF7WvAkrcdzmoasExTcAdLaYg6Pe7qSbCSETK7ZQTLbY68CbCCgVXNCyz3x+2KeHBrD2C1lSlY8spu5y8DsFQ3FFjSwQCrq2DJp9USLD6LXMy6CfVhTF7SprBksOLHL8McmZJAu64CWEmLsS6C5VYY+dAiPBcCrO6Dxed2Fay2SUwmK7AqnQMrmsLkAIDFnBByAIs+q/zdPMDSLaYHy5p4pYTOc2EHJxIsisx+A8vr9XQ4sKjx24FFNGGKdQFLQ7wRrI70saUAK933qj62X8AiXDuwDLEAS3I1IwVYnZsBVjJYFdkkAgCWDixTbBnBqpAjBVi8qwNLIMsUC7B4lwwIARZ3XvgVqtKq/A79AJs1WPEqNmAZY/sNrKrFuY73Cg2WUkLWYOm/vYxgcWSpvfCrACzJtQRLj5vL+ZOy/MBS3JRgSdXIBUlcAay2CgIWkWUCi07IASwmcgWw2uoCWOrYuwJWJVOwOq5MgJgPsFoqJlhcGH1WLZpIBks81nIKCQTQj1/kxEwPhA4qFFheI3UFS30xxC+mut5gVcgmfMFSXYAlyRss80DCgqXfkptQy80DLGkXYLVEjpQeiPDDAGURdbFSgCW2DbBis0KONDuw1EHrF3MFi26CSa4/WAlTkBqVkoQEejaaibkKYCUtlg1Ygsu0YLkUlggWt08mFBEs/tK9b8w/dMTR96jHmMBS9wAWf7P/FLhGTWAJ0xBmky9Y/KV7P7rvdatO3eMx5Zgug1W1cKvUap3t7MGSo9rbJrCUwsoDFn/p3o273FozDj5fOcgeLNcHkZBgqa4nWOZYJ7Bo1xcsIaBgYPGX7v31EZtqX6efqRzkARb9vVrhzLzA4qRpwi0WYJESLt1b15pdr1MO8gerQo60s1lmsBK+vYSKE2L1ExPNLoIlXLq3vr/XvKF4+2sfaWl/zZ1Tg2WDkNm1XKyDsQtYyvcB5VqAlVSYM1j2cxTNLoLFX7qXsQ1zhl/U5optjS/dO0NzZ0uw9JtZgaWc9p4By1CCULIY2wNg8ZfuZWtGznuWOijlU6F+0xUsYm0DWMqpdACLponaNIJlV5gnWJ3NAoLFX7p36AD1dXtDPmAZHjlsBtI1sOjF3MAiyUqagidYZGH0bDQTc5Xnz7G4S/cuia5eXNOjyjHeYFXIkQYCK2kxD7BschWwON+yMMNDiwYsYo7qbPIFi7t072XNq9ifpRxSfLDEM9kFsMRlhamQRzhOwQasipigziZnsCzkBVZFaJ12XRDyAEt2vcAimlAWEKZiAVbiFKr8dtnBks91W81bA4Elr8Yo0w2s5CZ4UweWfWE+YKkJ9hNzVI+AlTRSbWy5wZL/WyjGAiyrkWYAFu2mAEsf6wYW5SaAJd1eYrDcvlelgNBgyfcPApZbYQBLVppzrQvoObBYJTRYYuslAKuqq5ycaAHBqrKMwOLJEnyAZVT+YGlxc3h41INF9tQVsKg5crtU6wCrcGAJ9XYbrIQp0ASQrQOsPMAyLJYFWJSbCVjyXJXWdQ9ZujlqXWcBrP4ESxm07Rz1D4RuKitYzHYxqV4aLKcmOhYXIN4AsEwqKljEau3bswXLFEscoD3WDyzDq3f5HlyjfQhWypFaj8kDLKXaboNFuABLkiNYLHEg/QKW/ljS1ZbQ7jEBLHo2/Q5W5/Y+BYspBxiOzRksH7IAlvb8KfVxe9qzSjehA0u7bVGYHVg0WUy9uT/AovdTgeU6JtvFMgGLcm3Acp6CslFysNzOtRVYJtxcFuthsPTPhdIs+NkALCnRFSxmuVjmYImWvGlfGMCSVGiwCFcFS31BR4NljHUES3VTgKXMgtvvR7ASv1cNCb0HVgcn0vcGqyrKHEBPTDb7CCz+APuB2LpSbOL5CwmW0U8qjCoBYHH7RQRLLY/b1cfSTWQJlrqksC+BRZFFdgawXMHSrG63GA2WgVfbXBuwEgujSgBY3H4eYDGrxchXaWnAopbKDyx1NqaJuQhgiYsprj9Yxlgmg2U+VlOYXEL8kxAzWJqAPgYrYaTEHTqJIcGyXMwlVvB8wCJLAFjSvnxSxHtYg6Wu4w+WqVz9WdU2kR1YpilUJb/UYFHnWrxHYLCMi2UFFuUmgeU+BSNYVAKTEwCWHOAxpjzAopbS3WAszBos9SFL01k/g5VipFmBJbveYJljhcY0vjHBBBZZVmJnJQBLvovFQAoEFt1EVmCRK3KGaAOslnIAy7xYZmBRrhksc4AHWDZz7F2wNATE6iZY9GKSS54/C7ASYvnGNL45wRssbWf9D5Z6p+SB0LE+FEslaMEydBUQLM2KbUOyAVasroLF7BbLBixyMZorr8LIJ2npIOO3TH+DxaiJim6mYNks5gyWRW7b0oFlU5h4qw4skSxDQm+D1WPSPONkJporf0nVZVytgwCWWUmPeWkVGKyMq3VQBmDxl/HlBbCslCVW/QUWfxlfXn0BVq+pj8DiL+MrCGB1X+RLwlwqSQ8WfxlfQQCr++onsJTL+MYCWN1X0s9Buqf0YMmX8f16fOneg1NHQ67qJ7DEy/gy9uK6lsjLrkIlUXqwhMv4QlBT6cHiL+MLQS1l8HOs+DK+ENRRFr/SaV3GF4I6Cvi7QqjMAlhQEAEsKIgAFhREAAsKIoAFBRHAgoIoR7BePGQ2oemHUu67JlPuxPdS7tuPpdwJlEm7Mw6m3KMOo9zDp1LuITNSlvD+t1Pue/+aco+cRLl/827KPfB99oUdmers5gjWpo9S7r3nU+4Pr6Xcc39JuSdtplz6TTyk++QnKff271HuJYso9zO/pdz3b7cuYcsJlPu/X6Lc68lf035zGeWe8px1CSnf9gSwVAEsg2srgKUKYBlcWwEsVQDL4NoKYKkCWAbXVgBLFcAyuLYCWKoAlsG1FcBSBbAMrq1yBOvPX6PcR8gx3baYci9/nHLPId90eBZZA+luvJByV/+Icm9YSbkXkX9I8oUd1iW8/mXKfepSyl1Ksn3VWso9n/ymc5iNtfArHSiIABYURAALCiKABQURwIKCCGBBQQSwoCACWFAQASwoiAAWFEQACwqiHgdrG/nrt35RL3eXJ1hzH1GsN+YfOuLoe2R3w8lv22fuk0TCwwOrZevyqK5nZPu2mSPec79oLWocGU2Vjnzlc+NGzl0n3//lT40f9y9bqAaUj7mP2xLba+zJ7TVMpbv4jmJ3DVfprnms3F3dVdtrHKu013CF9uIqdR/gb6UcwVoYPah4H933ulWn7vGYaO6YNuXOm6dMfEM5+vVJkQLW2ccsqkl+g8OigUuWzhv9kuBtrB946+BXpENPnfDT5TMP3SaVMPOIJUunHUs1IH/MfdyW2F5zT2qvYSrdxXcUu2u6cndNV+6u4SrtNY+V22vWILQXV6n7AH8r5QbWDYORCtbGXW5lbOhg6S1Zj0VPMLY0+pWSMX+aCtbcc6jVJi+ofVfO+bl6w8JBicGhge8x9kQkPbqtiJ5i7PndH1AbkD7mPm5LbK+1J7bXMqXuOnfku4tdsbvYFbvjl+60F5crttdyhfbiKrUf4G+l3MBat/gaFaxfH7Gp9nX6maL7+4uHGHswWi8ffdeYNSpYh1+x5XllsSejh+kqXh63XHKG3nIpY+vl0q7aq/518PtqA9LH3Mdtie219sT2WqbUXfuOQnexK3bXcqXuuKW59lqu1F7LFdqLq9R+gL+VcnwqfIZ4Kqxrza7XKd69l0/8tOz9cdzPnlXBGj5lWLTfQslcHt1y5Ijp96prffV4xTr9oBuXHffON0XzzmgDY38aEN9+12hA+Zj7uC2xvc4e115sit01Xbm7pit313CV7tqLCe01Xbm9hqu2V6tS+wH+VioeWDfuNW9IMRccMfy7svfhM5kK1qaBTz27af5ua0R3YXTQ9fedPvCEHLFpzwdki720XxQNk9/z/Po73nv33TN3E+FuNCB/zH0SWHx7sSl213Tl7hqu0l3DVbqLc8X2mq7cXsNV2qtXqXTmpKKBtWHO8ItUrmpavftdonHNIVsIsBraMVF6oXV7dAdj2wfPkw/8+jHKfbcMnvHc5kvf+pBc7LyRh1xy5L+JXr0B+WPuzWCJ7XUO4btruEp3nWP57hqu0l18rNhew1Xaax4rttesUunMSQUDa83IeerbxVc3/pLiXeJZZZ+hf1rQ0In/JO4/ENX/iODE06TDtk+4Urnrwj3qTxOzziZSd4wW34/faED5mHsTWFJ7DVPpruEq3XFxXHcNV+mudazUXvPRTW6PAzZur1Vlug/wLxZYQwecSRz432/Zyti2ff9TdDesXbt2SXSt9PcUNx9UexX85oHSX9S8NuoWxt44QP4jiaXD1L8tuGn3P9e+TpH+HualObUHj2W7i/8taDSgfMy9ASy5vYapdNdwle4artJdw1W6ay0ttddwlfYartBeXGW6D/AvFlhLoqsX1/So6G4+cO6K+04e/Qc1Qn0qfHn8MYvuPXk/GZgLxv7kFx/b5wXJ/coRauSWwWOXrf7sHvLPbmdMuu3WCV+gGpA/5t4Altxew1S6a99RfSpUumseK3fXSpDaaz0VSu01j+Xba1eZ6gP8iwXWZc0nAPkPj545ab/RJ/yGiCBeY238+Ph9/+F3ypHfmrj3nEdlc/Lnicz1p4zde7YS+/yH9jrsQuk3LK0GpI+5N4Alt9d6fSN1ZwBL6a51rNRdy5Xaa7pye02Xb69TZZoP8O/x3xVCRRXAgoIIYEFBBLCgIAJYUBABLCiIABYURAALCiKABQURwIKCCGBBQQSwoCACWFAQASwoiAAWFEQACwoigOWs0+pvgxv78Q2yPxStZOtfbPwDASx3nTZ55cq7vzHmKPkTO7aftY7NXtD4BwJY7jptVv3r9dHTxG01sKCGAJazmmAtjh5iL58xYfTc39W2jxo+/qs7WLRyav0PtmpPhfENe948Zfj4y/MuOBcBLGfVwXpz6bQxr7OZh/3PPR8c9+rzA59f9YNdrqsRte3Y87bVwWrdwPYcc9XaT0ePJWf2nwCWsxov3qNxK9iq+qdmbBl51fL6h3IsebhOVP2pMFoZ38D2PI+xV3a/Pe+K8xDAclb9xfvKX21j7Mqx9d3j5r8246/mXvw464AV31B7Kqz9uyfAgmzUfI3FYrA+eBbb8YsLZg67TAardkODKYAFWakN1n31V09bR12x/Ju1nS//bQes+AaABTmoDRabMYE8XLgAAAC4SURBVOmuVX8/buuKXS586ObDz6iD9YFT/lD/p3UDwIIc1AFr8xnjR534NGNXTBwY//ktdaJ+/Lb31f+JbwBYEJStABYURAALCiKABQURwIKCCGBBQQSwoCACWFAQASwoiAAWFEQACwoigAUFEcCCgghgQUEEsKAgAlhQEAEsKIgAFhREAAsKIoAFBRHAgoIIYEFBBLCgIAJYUBABLCiIABYURAALCiKABQURwIKCCGBBQQSwoCD6f9UK5ROQ/WtrAAAAAElFTkSuQmCC" /></p>
+<p>You may need to install <em>seqLogo</em> before</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="cf">if</span> (<span class="sc">!</span><span class="fu">requireNamespace</span>(<span class="st">"BiocManager"</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>))</span>
+<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">install.packages</span>(<span class="st">"BiocManager"</span>)</span>
+<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>BiocManager<span class="sc">::</span><span class="fu">install</span>(<span class="st">"seqLogo"</span>)</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" aria-hidden="true" tabindex="-1"></a><span class="fu">plotAnc</span>(tree, anc.mpr, <span class="dv">17</span>)</span>
+<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="fu">title</span>(<span class="st">"MPR"</span>)</span></code></pre></div>
+<div class="figure">
+<img 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" alt />
+<p class="caption">Fig 2. Ancestral reconstruction using MPR.</p>
+</div>
+<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="fu">plotAnc</span>(tree, anc.acctran, <span class="dv">17</span>)</span>
+<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="fu">title</span>(<span class="st">"ACCTRAN"</span>)</span></code></pre></div>
+<div class="figure">
+<img 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" alt />
+<p class="caption">Fig 3. Ancestral reconstruction using ACCTRAN.</p>
+</div>
+</div>
+<div id="likelihood-reconstructions" class="section level1">
+<h1>Likelihood reconstructions</h1>
+<p><em>phangorn</em> also offers the possibility to estimate ancestral
+states using a ML. The advantages of ML over parsimony is that the
+reconstruction accounts for different edge lengths. So far only a
+marginal construction is implemented (see <span class="citation">(Yang
+2006)</span>).</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>fit <span class="ot"><-</span> <span class="fu">pml</span>(tree, primates)</span>
+<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>fit <span class="ot"><-</span> <span class="fu">optim.pml</span>(fit, <span class="at">model=</span><span class="st">"F81"</span>, <span class="at">control =</span> <span class="fu">pml.control</span>(<span class="at">trace=</span><span class="dv">0</span>))</span></code></pre></div>
+<p>We can assign the ancestral states according to the highest
+likelihood (“ml”): <span class="math display">\[
+P(x_r = A) = \frac{L(x_r=A)}{\sum_{k \in \{A,C,G,T\}}L(x_r=k)}
+\]</span> and the highest posterior probability (“bayes”) criterion:
+<span class="math display">\[
+P(x_r=A) = \frac{\pi_A L(x_r=A)}{\sum_{k \in \{A,C,G,T\}}\pi_k
+L(x_r=k)},
+\]</span> where <span class="math inline">\(L(x_r)\)</span> is the joint
+probability of states at the tips and the state at the root <span class="math inline">\(x_r\)</span> and <span class="math inline">\(\pi_i\)</span> are the estimated base frequencies
+of state <span class="math inline">\(i\)</span>. Both methods agree if
+all states (base frequencies) have equal probabilities.</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>anc.ml <span class="ot"><-</span> <span class="fu">ancestral.pml</span>(fit, <span class="st">"ml"</span>)</span>
+<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>anc.bayes <span class="ot"><-</span> <span class="fu">ancestral.pml</span>(fit, <span class="st">"bayes"</span>)</span></code></pre></div>
+<p>The differences of the two approaches for a specific site (17) are
+represented in the following figures.</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">plotAnc</span>(tree, anc.ml, <span class="dv">17</span>)</span>
+<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="fu">title</span>(<span class="st">"ML"</span>)</span></code></pre></div>
+<div class="figure">
+<img 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" alt />
+<p class="caption">Fig 4. Ancestral reconstruction the using the maximum
+likelihood.</p>
+</div>
+<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">plotAnc</span>(tree, anc.bayes, <span class="dv">17</span>)</span>
+<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a><span class="fu">title</span>(<span class="st">"Bayes"</span>)</span></code></pre></div>
+<div class="figure">
+<img 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" alt />
+<p class="caption">Fig 5. Ancestral reconstruction using (empirical)
+Bayes.</p>
+</div>
+</div>
+<div id="session-info" class="section level1">
+<h1>Session info</h1>
+<pre><code>## R version 4.2.2 Patched (2022-11-10 r83330)
+## Platform: x86_64-pc-linux-gnu (64-bit)
+## Running under: Debian GNU/Linux bookworm/sid
+##
+## Matrix products: default
+## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0
+## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
+##
+## locale:
+## [1] C
+##
+## attached base packages:
+## [1] stats graphics grDevices utils datasets methods base
+##
+## other attached packages:
+## [1] phangorn_2.12.0.900 ape_5.6-2
+##
+## loaded via a namespace (and not attached):
+## [1] igraph_1.3.5 Rcpp_1.0.10 knitr_1.41 magrittr_2.0.3
+## [5] lattice_0.20-45 R6_2.5.1 quadprog_1.5-8 rlang_1.0.6
+## [9] fastmatch_1.1-3 fastmap_1.1.0 highr_0.10 stringr_1.5.0
+## [13] tools_4.2.2 parallel_4.2.2 grid_4.2.2 nlme_3.1-161
+## [17] xfun_0.36 cli_3.6.0 jquerylib_0.1.4 htmltools_0.5.4
+## [21] yaml_2.3.6 digest_0.6.31 lifecycle_1.0.3 Matrix_1.5-3
+## [25] codetools_0.2-18 sass_0.4.4 vctrs_0.5.1 glue_1.6.2
+## [29] cachem_1.0.6 evaluate_0.20 rmarkdown_2.20 stringi_1.7.12
+## [33] compiler_4.2.2 bslib_0.4.2 generics_0.1.3 jsonlite_1.8.4
+## [37] pkgconfig_2.0.3</code></pre>
+</div>
+<div id="references" class="section level1 unnumbered">
+<h1 class="unnumbered">References</h1>
+<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="ref-Felsenstein2004" class="csl-entry">
+Felsenstein, Joseph. 2004. <em>Inferring Phylogenies</em>. Sunderland:
+Sinauer Associates.
+</div>
+<div id="ref-Schliep2011" class="csl-entry">
+Schliep, Klaus Peter. 2011. <span>“Phangorn: Phylogenetic Analysis in
+<span>R</span>.”</span> <em>Bioinformatics</em> 27 (4): 592–93. <a href="https://doi.org/10.1093/bioinformatics/btq706">https://doi.org/10.1093/bioinformatics/btq706</a>.
+</div>
+<div id="ref-Swofford1987" class="csl-entry">
+Swofford, D. L., and W. P. Maddison. 1987. <span>“Reconstructing
+Ancestral Character States Under Wagner Parsimony.”</span> <em>Math.
+Biosci.</em> 87: 199–229.
+</div>
+<div id="ref-Yang2006" class="csl-entry">
+Yang, Ziheng. 2006. <em>Computational Molecular Evolution</em>. Oxford:
+Oxford University Press.
+</div>
+</div>
+</div>
+
+
+
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+
+
+
+<h1 class="title toc-ignore">Intertwining phylogenetic trees and
+networks: R Example Script</h1>
+<h4 class="author">Klaus Schliep, Alastair Potts, David Morrison and
+Guido Grimm</h4>
+<h4 class="date">January 27, 2023</h4>
+
+
+
+<p><em>Description:</em> This script provides examples of the new
+functions available in the phangorn library to ‘intertwine’ trees and
+networks, i.e. compare trees and networks and data transferrance. It
+also provides a step-by-step guide for users new to R.</p>
+<p><em>Methodological advancement:</em> The major advancement in this
+phangorn update is the introduction of a generic network object with a
+wide range of related transfer and analysis functions. These new
+functions provide the first means to directly transfer information
+amongst a wide range of phylogenetic trees and networks, as well as
+means to visualize and further analyze this information. This should
+provide a platform for individuals to easily conduct tree-network
+comparisons and stimulate further function development by the
+community.</p>
+<p><em>What next?:</em> By implementing full network handling
+compatibility in R, and providing exemplar scripts (such as this) and <a href="https://github.com/KlausVigo/phangorn">support</a>, the scientific
+community now has an easy means to analyse and compare the results of
+phylogenetic trees vs. network approaches. We hope this will open a
+largely unexplored world to the general phylogenetic audience.</p>
+<div id="installing-r" class="section level3">
+<h3>Installing R</h3>
+<ol style="list-style-type: decimal">
+<li>Download R<br />
+Select the nearest mirror to your location at <a href="https://cran.r-project.org/mirrors.html" class="uri">https://cran.r-project.org/mirrors.html</a><br />
+</li>
+<li>Select your operating system and download the relevant installation
+file.<br />
+</li>
+<li>Install R following the instructions.</li>
+</ol>
+</div>
+<div id="installing-the-phangorn-library" class="section level3">
+<h3>Installing the phangorn library</h3>
+<p>Open R and run the two lines of code below in the command line.<br />
+(You will need to select a region from which to download the
+library)</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">install.packages</span>(<span class="st">"phangorn"</span>, <span class="at">dependencies=</span><span class="cn">TRUE</span>)</span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="co"># install latest development version needs devtools</span></span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"devtools"</span>, <span class="at">dependencies=</span><span class="cn">TRUE</span>)</span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(devtools)</span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu">install_github</span>(<span class="st">"KlausVigo/phangorn"</span>)</span></code></pre></div>
+</div>
+<div id="getting-started" class="section level3">
+<h3>Getting started</h3>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(phangorn) <span class="co"># load the phangorn library</span></span></code></pre></div>
+</div>
+<div id="set-the-working-directory" class="section level3">
+<h3>Set the working directory</h3>
+<p>This is often a major stumbling block for new R users. You need to
+specify where in your folder structure you wish to work. i.e, where are
+the files stored that you wish to input?</p>
+<p>This is done using the <code>setwd()</code> function,
+e.g. <code>setwd("C:/TreesNetworks/Example Files")</code>.</p>
+<p>We now set it to the folder of the phangorn package, which contains
+the files we want to load for this example.</p>
+</div>
+<div id="read-in-the-example-file-datasets" class="section level3">
+<h3>Read in the example file datasets:</h3>
+<p>This example files are based on the woodmouse dataset available in
+the ape library. Ultimately, this dataset is from this study: Michaux,
+J. R., Magnanou, E., Paradis, E., Nieberding, C. and Libois, R. (2003)
+Mitochondrial phylogeography of the Woodmouse (<em>Apodemus
+sylvaticus</em>) in the Western Palearctic region. Molecular Ecology,
+12, 685-697.)</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="do">## automatically set the correct working directory for the examples below</span></span>
+<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="co"># setwd(system.file("extdata/trees", package = "phangorn"))</span></span>
+<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="co"># for this vignette we create a path to the files we want to load </span></span>
+<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>fdir <span class="ot"><-</span> <span class="fu">system.file</span>(<span class="st">"extdata/trees"</span>, <span class="at">package =</span> <span class="st">"phangorn"</span>)</span>
+<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a><span class="do">## in your case it may look something like this...</span></span>
+<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a><span class="co"># setwd("C:/TreesNetworks/Example Files")</span></span>
+<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a><span class="do">##DNA Matrix, maybe not needed </span></span>
+<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a>woodmouse <span class="ot"><-</span> <span class="fu">read.phyDat</span>(<span class="fu">file.path</span>(fdir, <span class="st">"woodmouse.fasta"</span>),<span class="at">format=</span><span class="st">"fasta"</span>) </span>
+<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a><span class="do">## RAxML best-known tree with bipartition support (from previous analysis)</span></span>
+<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a>raxml.tree <span class="ot"><-</span> <span class="fu">read.tree</span>(<span class="fu">file.path</span>(fdir,<span class="st">"RAxML_bipartitions.woodmouse"</span>))</span>
+<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a><span class="do">## RAxML bootstrap trees (from previous analysis)</span></span>
+<span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a>raxml.bootstrap <span class="ot"><-</span> <span class="fu">read.tree</span>(<span class="fu">file.path</span>(fdir,<span class="st">"RAxML_bootstrap.woodmouse"</span>))</span>
+<span id="cb3-14"><a href="#cb3-14" aria-hidden="true" tabindex="-1"></a><span class="do">## MrBayes consensus tree (50% majority rule) (from previous analysis)</span></span>
+<span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a>mrbayes.tree <span class="ot"><-</span> <span class="fu">read.nexus</span>(<span class="fu">file.path</span>(fdir,<span class="st">"woodmouse.mrbayes.nex.con"</span>))</span>
+<span id="cb3-16"><a href="#cb3-16" aria-hidden="true" tabindex="-1"></a><span class="do">## MrBayes sample runs 1 and 2 (from previous analysis)</span></span>
+<span id="cb3-17"><a href="#cb3-17" aria-hidden="true" tabindex="-1"></a>run1 <span class="ot"><-</span> <span class="fu">read.nexus</span>(<span class="fu">file.path</span>(fdir,<span class="st">"woodmouse.mrbayes.nex.run1.t"</span>))</span>
+<span id="cb3-18"><a href="#cb3-18" aria-hidden="true" tabindex="-1"></a>run2 <span class="ot"><-</span> <span class="fu">read.nexus</span>(<span class="fu">file.path</span>(fdir,<span class="st">"woodmouse.mrbayes.nex.run2.t"</span>))</span>
+<span id="cb3-19"><a href="#cb3-19" aria-hidden="true" tabindex="-1"></a><span class="do">## How many trees are in the MrBayes tree sample?</span></span>
+<span id="cb3-20"><a href="#cb3-20" aria-hidden="true" tabindex="-1"></a>run1</span></code></pre></div>
+<pre><code>## 1001 phylogenetic trees</code></pre>
+<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>run2</span></code></pre></div>
+<pre><code>## 1001 phylogenetic trees</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="do">## Combining the two runs and removing 25% burn-in</span></span>
+<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>mrbayes.trees <span class="ot"><-</span> <span class="fu">c</span>(run1[<span class="dv">251</span><span class="sc">:</span><span class="dv">1001</span>],run2[<span class="dv">251</span><span class="sc">:</span><span class="dv">1001</span>])</span>
+<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="do">## NeigbourNet Nexus file generated by SplitsTree (from previous analysis)</span></span>
+<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>Nnet <span class="ot"><-</span> <span class="fu">read.nexus.networx</span>(<span class="fu">file.path</span>(fdir,<span class="st">"woodmouse.nxs"</span>))</span></code></pre></div>
+<p>All example files read into R.</p>
+</div>
+<div id="viewing-the-data" class="section level3">
+<h3>Viewing the data</h3>
+<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="fu">par</span>(<span class="at">mfrow=</span><span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>), <span class="at">mar=</span><span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>)) <span class="co"># Setting plot parameters</span></span>
+<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="do">### Plotting trees with support values:</span></span>
+<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="do">## RAxML</span></span>
+<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(raxml.tree)</span>
+<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a><span class="fu">nodelabels</span>(raxml.tree<span class="sc">$</span>node.label, <span class="at">adj =</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">0</span>), <span class="at">frame =</span> <span class="st">"none"</span>)</span>
+<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a><span class="do">## MrBayes</span></span>
+<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mrbayes.tree)</span>
+<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a><span class="fu">nodelabels</span>(mrbayes.tree<span class="sc">$</span>node.label, <span class="at">adj =</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">0</span>), <span class="at">frame =</span> <span class="st">"none"</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,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" /><!-- --></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="fu">par</span>(<span class="at">mfrow=</span><span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">1</span>)) <span class="co"># Setting plot parameters</span></span>
+<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="co"># NeighborNet</span></span>
+<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(Nnet,<span class="st">"2D"</span>)</span></code></pre></div>
+<p><img 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" /><!-- --></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="do">## alternatively,</span></span>
+<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="co"># plot(Nnet,"2D")</span></span></code></pre></div>
+</div>
+<div id="intertwining-trees-and-network-functions" class="section level3">
+<h3>Intertwining trees and network functions</h3>
+</div>
+<div id="a" class="section level3">
+<h3>1A:</h3>
+<p>Identification of edge bundles (in black) in a neighbor-net (NN)
+network that correspond to branches (labelled 1-12) in a tree (a maximum
+likelihood tree, in this case).</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="co"># create a vector of labels for the network corresponding to edges in the tree</span></span>
+<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>edge.lab <span class="ot"><-</span> <span class="fu">createLabel</span>(Nnet, raxml.tree, raxml.tree<span class="sc">$</span>edge[,<span class="dv">2</span>], <span class="st">"edge"</span>)</span>
+<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a><span class="co"># could be also 1:27 instead of raxml.tree$edge[,2]</span></span>
+<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Show the correspondingly labelled tree and network in R</span></span>
+<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a><span class="fu">par</span>(<span class="at">mfrow=</span><span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>)) </span>
+<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(raxml.tree, <span class="st">"u"</span>, <span class="at">rotate.tree =</span> <span class="dv">180</span>, <span class="at">cex=</span>.<span class="dv">7</span>) </span>
+<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a><span class="fu">edgelabels</span>(raxml.tree<span class="sc">$</span>edge[,<span class="dv">2</span>],<span class="at">col=</span><span class="st">"blue"</span>, <span class="at">frame=</span><span class="st">"none"</span>, <span class="at">cex=</span>.<span class="dv">7</span>)</span>
+<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a><span class="co"># find edges that are in the network but not in the tree</span></span>
+<span id="cb11-11"><a href="#cb11-11" aria-hidden="true" tabindex="-1"></a>edge.col <span class="ot"><-</span> <span class="fu">rep</span>(<span class="st">"black"</span>, <span class="fu">nrow</span>(Nnet<span class="sc">$</span>edge))</span>
+<span id="cb11-12"><a href="#cb11-12" aria-hidden="true" tabindex="-1"></a>edge.col[ <span class="fu">is.na</span>(edge.lab) ] <span class="ot"><-</span> <span class="st">"red"</span></span>
+<span id="cb11-13"><a href="#cb11-13" aria-hidden="true" tabindex="-1"></a><span class="co"># or a simpler alternative...</span></span>
+<span id="cb11-14"><a href="#cb11-14" aria-hidden="true" tabindex="-1"></a>edge.col <span class="ot"><-</span> <span class="fu">createLabel</span>(Nnet, raxml.tree, <span class="st">"black"</span>, <span class="at">nomatch=</span><span class="st">"red"</span>)</span>
+<span id="cb11-15"><a href="#cb11-15" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb11-16"><a href="#cb11-16" aria-hidden="true" tabindex="-1"></a>x <span class="ot"><-</span> <span class="fu">plot</span>(Nnet, <span class="at">edge.label =</span> edge.lab, <span class="at">show.edge.label =</span> T, <span class="st">"2D"</span>, </span>
+<span id="cb11-17"><a href="#cb11-17" aria-hidden="true" tabindex="-1"></a> <span class="at">edge.color =</span> edge.col, <span class="at">col.edge.label =</span> <span class="st">"blue"</span>, <span class="at">cex=</span>.<span class="dv">7</span>)</span></code></pre></div>
+<p><img 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" /><!-- --></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="co"># the above plot function returns an invisible networx object and this object </span></span>
+<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="co"># also contains the colors for the edges.</span></span></code></pre></div>
+</div>
+<div id="b" class="section level3">
+<h3>1B:</h3>
+<p>Bootstrap support for all branches (branch labels) in the ML tree
+mapped on the corresponding edge bundles of the NN network. The edges in
+the network which are not found as ML tree branches are highlighted in
+red.</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"># the scaler argument multiplies the confidence values. This is useful to switch</span></span>
+<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a><span class="co"># confidences values between total, percentage or ratios. </span></span>
+<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>x <span class="ot"><-</span> <span class="fu">addConfidences</span>(Nnet,raxml.tree, <span class="at">scaler =</span> .<span class="dv">01</span>)</span>
+<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a><span class="co"># find splits that are in the network but not in the tree</span></span>
+<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a>split.col <span class="ot"><-</span> <span class="fu">rep</span>(<span class="st">"black"</span>, <span class="fu">length</span>(x<span class="sc">$</span>splits))</span>
+<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a>split.col[ <span class="sc">!</span><span class="fu">matchSplits</span>(<span class="fu">as.splits</span>(x), <span class="fu">as.splits</span>(raxml.tree)) ] <span class="ot"><-</span> <span class="st">"red"</span></span>
+<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a><span class="co"># simpler alternative...</span></span>
+<span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a>split.col2 <span class="ot"><-</span> <span class="fu">createLabel</span>(x, raxml.tree, <span class="at">label=</span><span class="st">"black"</span>, <span class="st">"split"</span>, <span class="at">nomatch=</span><span class="st">"red"</span>)</span>
+<span id="cb13-10"><a href="#cb13-10" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb13-11"><a href="#cb13-11" aria-hidden="true" tabindex="-1"></a><span class="co"># Plotting in R</span></span>
+<span id="cb13-12"><a href="#cb13-12" aria-hidden="true" tabindex="-1"></a>out.x <span class="ot"><-</span> <span class="fu">plot</span>(x,<span class="st">"2D"</span>,<span class="at">show.edge.label=</span><span class="cn">TRUE</span>, <span class="at">split.color=</span>split.col, </span>
+<span id="cb13-13"><a href="#cb13-13" aria-hidden="true" tabindex="-1"></a> <span class="at">col.edge.label =</span> <span class="st">"blue"</span>)</span></code></pre></div>
+<p><img 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" /><!-- --></p>
+<p>Again we can write to SplitsTree for viewing…</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><span class="co"># write.nexus.networx(out.x,"woodmouse.tree.support.nxs")</span></span>
+<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a><span class="do">## or we can also export the splits alone (for usage in software other than SplitsTree)</span></span>
+<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a><span class="co"># write.nexus.splits(as.splits(out.x),"woodmouse.splits.support.nxs")</span></span></code></pre></div>
+</div>
+<div id="c" class="section level3">
+<h3>1C:</h3>
+<p>Frequencies of bipartitions found in the bootstrap pseudoreplicates
+mapped on the corresponding edge bundles of the NN network using a
+threshold of 10% (i.e. any edge is labelled that occurs in at least 100
+of the 1000 ML-BS pseudoreplicates). Edge bundles not found in the ML
+tree are labelled using grey edges.</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>y <span class="ot"><-</span> <span class="fu">addConfidences</span>(Nnet, <span class="fu">as.splits</span>(raxml.bootstrap))</span>
+<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>edge.col <span class="ot"><-</span> <span class="fu">createLabel</span>(y, raxml.tree, <span class="at">label=</span><span class="st">"black"</span>, <span class="st">"edge"</span>, <span class="at">nomatch=</span><span class="st">"grey"</span>)</span>
+<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>y <span class="ot"><-</span> <span class="fu">plot</span>(y,<span class="st">"2D"</span>,<span class="at">show.edge.label=</span><span class="cn">TRUE</span>, <span class="at">edge.color=</span>edge.col)</span></code></pre></div>
+<p><img 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" /><!-- --></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="do">## Write to SplitsTree for viewing</span></span>
+<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="co"># write.nexus.networx(y,"NN.with.bs.support.nxs")</span></span></code></pre></div>
+</div>
+<div id="extras" class="section level3">
+<h3>Extras…</h3>
+<p>We can also compare the neighborNet with a consensusNet (Holland BR,
+Huber KT, Moulton V, Lockhart PJ,2004, Using consensus networks to
+visualize contradictory evidence for species phylogeny. Molecular
+Biology and Evolution, 21, 1459-1461). Furthermore, we can extract the
+support values from the consensus network, and place these on to the
+NeighborNet (this is similar to the process explained in 1C above).</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>cnet <span class="ot"><-</span> <span class="fu">consensusNet</span>(raxml.bootstrap,<span class="at">prob=</span><span class="fl">0.10</span>)</span>
+<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>edge.col <span class="ot"><-</span> <span class="fu">createLabel</span>(cnet, Nnet, <span class="at">label=</span><span class="st">"black"</span>, <span class="st">"edge"</span>, <span class="at">nomatch=</span><span class="st">"grey"</span>)</span>
+<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>cnet <span class="ot"><-</span> <span class="fu">plot</span>(cnet, <span class="st">"2D"</span>, <span class="at">show.edge.label =</span> <span class="cn">TRUE</span>, <span class="at">edge.color=</span>edge.col)</span></code></pre></div>
+<p><img 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" /><!-- --></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>edge.col <span class="ot"><-</span> <span class="fu">createLabel</span>(Nnet, cnet, <span class="at">label=</span><span class="st">"black"</span>, <span class="st">"edge"</span>, <span class="at">nomatch=</span><span class="st">"grey"</span>)</span>
+<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>z <span class="ot"><-</span> <span class="fu">plot</span>(Nnet, <span class="st">"2D"</span>, <span class="at">show.edge.label =</span> <span class="cn">TRUE</span>, <span class="at">edge.color=</span>edge.col)</span></code></pre></div>
+<p><img 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" /><!-- --></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>obj <span class="ot"><-</span> <span class="fu">addConfidences</span>(Nnet,cnet)</span>
+<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(obj,<span class="st">"2D"</span>,<span class="at">show.edge.label=</span>T, <span class="at">edge.color=</span>edge.col, <span class="at">col.edge.label =</span> <span class="st">"blue"</span>)</span></code></pre></div>
+<p><img 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" /><!-- --></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="do">## Write to SplitsTree for viewing</span></span>
+<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a><span class="co"># write.nexus.networx(obj,"Nnet.with.ML.Cnet.Bootstrap.nxs")</span></span></code></pre></div>
+</div>
+<div id="section" class="section level3">
+<h3></h3>
+<p>There are four possible data patterns in phylogenetic reconstruction:
+(1) patterns that are well supported in the network and appear in the
+bootstrapped trees; (2) patterns that are well supported by (part of)
+the data/network but do not appear in the optimized trees, i.e. they are
+incompatible with the tree; (3) patterns that are weakly supported by
+the network but appear in the optimized trees anyway, i.e. they are
+compatible with the tree.</p>
+<p>Here we demonstrate these patterns by showing the relationships
+between splits weights, from a NeighborNet splits graph, bootstrap
+bipartitions support and bootstrap percentages plotted on the optimized
+tree for a dataset of <span class="citation">Wang, Braun, and Kimball
+(2012)</span>.</p>
+<p>(An advanced user figure…)</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>Nnet <span class="ot"><-</span> <span class="fu">read.nexus.networx</span>(<span class="fu">file.path</span>(fdir,<span class="st">"RAxML_distances.Wang.nxs"</span>))</span>
+<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>raxml.tree <span class="ot"><-</span> <span class="fu">read.tree</span>(<span class="fu">file.path</span>(fdir,<span class="st">"RAxML_bestTree.Wang.out"</span>)) <span class="sc">|></span> <span class="fu">unroot</span>()</span>
+<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>raxml.bootstrap <span class="ot"><-</span> <span class="fu">read.tree</span>(<span class="fu">file.path</span>(fdir,<span class="st">"RAxML_bootstrap.Wang.out"</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>bs_splits <span class="ot"><-</span> <span class="fu">as.splits</span>(raxml.bootstrap)</span>
+<span id="cb21-6"><a href="#cb21-6" aria-hidden="true" tabindex="-1"></a>tree_splits <span class="ot"><-</span> <span class="fu">as.splits</span>(raxml.tree) <span class="sc">|></span> <span class="fu">unique</span>() <span class="sc">|></span> <span class="fu">removeTrivialSplits</span>()</span>
+<span id="cb21-7"><a href="#cb21-7" aria-hidden="true" tabindex="-1"></a><span class="co"># we overwrite bootstrap values and set the weights </span></span>
+<span id="cb21-8"><a href="#cb21-8" aria-hidden="true" tabindex="-1"></a><span class="co"># to 1e-6 (almost zero), as we plot them on a log scale later on</span></span>
+<span id="cb21-9"><a href="#cb21-9" aria-hidden="true" tabindex="-1"></a><span class="fu">attr</span>(bs_splits, <span class="st">"weights"</span>)[] <span class="ot"><-</span> <span class="fl">1e-6</span></span>
+<span id="cb21-10"><a href="#cb21-10" aria-hidden="true" tabindex="-1"></a><span class="co"># combine the splits from the bootstrap and neighbor net</span></span>
+<span id="cb21-11"><a href="#cb21-11" aria-hidden="true" tabindex="-1"></a><span class="co"># and delete duplicates and add the confidence values</span></span>
+<span id="cb21-12"><a href="#cb21-12" aria-hidden="true" tabindex="-1"></a><span class="co"># we get rid of trivial splits</span></span>
+<span id="cb21-13"><a href="#cb21-13" aria-hidden="true" tabindex="-1"></a>all_splits <span class="ot"><-</span> <span class="fu">c</span>(Nnet<span class="sc">$</span>splits, bs_splits) <span class="sc">|></span> <span class="fu">unique</span>() <span class="sc">|></span> <span class="fu">removeTrivialSplits</span>() <span class="sc">|></span> </span>
+<span id="cb21-14"><a href="#cb21-14" aria-hidden="true" tabindex="-1"></a> <span class="fu">addConfidences</span>(bs_splits, <span class="at">scaler=</span><span class="dv">100</span>)</span>
+<span id="cb21-15"><a href="#cb21-15" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb21-16"><a href="#cb21-16" aria-hidden="true" tabindex="-1"></a><span class="co"># For easier plotting we create a matrix with the confidences and </span></span>
+<span id="cb21-17"><a href="#cb21-17" aria-hidden="true" tabindex="-1"></a><span class="co"># weights as columns</span></span>
+<span id="cb21-18"><a href="#cb21-18" aria-hidden="true" tabindex="-1"></a>tab <span class="ot"><-</span> <span class="fu">data.frame</span>(<span class="at">SplitWeight =</span> <span class="fu">attr</span>(all_splits, <span class="st">"weights"</span>), </span>
+<span id="cb21-19"><a href="#cb21-19" aria-hidden="true" tabindex="-1"></a> <span class="at">Bootstrap=</span><span class="fu">attr</span>(all_splits, <span class="st">"confidences"</span>), <span class="at">Tree=</span><span class="cn">FALSE</span>)</span>
+<span id="cb21-20"><a href="#cb21-20" aria-hidden="true" tabindex="-1"></a><span class="co"># we add a logical variable pto indicate which splits are in the RAxML tree</span></span>
+<span id="cb21-21"><a href="#cb21-21" aria-hidden="true" tabindex="-1"></a>tab<span class="sc">$</span>Tree[<span class="fu">matchSplits</span>(tree_splits, all_splits, <span class="cn">FALSE</span>)] <span class="ot"><-</span> <span class="cn">TRUE</span></span>
+<span id="cb21-22"><a href="#cb21-22" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb21-23"><a href="#cb21-23" aria-hidden="true" tabindex="-1"></a>tab[<span class="fu">is.na</span>(tab[,<span class="st">"Bootstrap"</span>]),<span class="st">"Bootstrap"</span>] <span class="ot"><-</span> <span class="dv">0</span></span>
+<span id="cb21-24"><a href="#cb21-24" aria-hidden="true" tabindex="-1"></a>tab[,<span class="st">"Bootstrap"</span>] <span class="ot"><-</span> <span class="fu">round</span>(tab[,<span class="st">"Bootstrap"</span>])</span>
+<span id="cb21-25"><a href="#cb21-25" aria-hidden="true" tabindex="-1"></a><span class="fu">rownames</span>(tab) <span class="ot"><-</span> <span class="fu">apply</span>(<span class="fu">as.matrix</span>(all_splits, <span class="at">zero.print =</span> <span class="st">"."</span>, <span class="at">one.print =</span> <span class="st">"|"</span>), </span>
+<span id="cb21-26"><a href="#cb21-26" aria-hidden="true" tabindex="-1"></a> <span class="dv">1</span>, paste0, <span class="at">collapse=</span><span class="st">""</span>)</span>
+<span id="cb21-27"><a href="#cb21-27" aria-hidden="true" tabindex="-1"></a>tab[<span class="dv">1</span><span class="sc">:</span><span class="dv">10</span>,]</span></code></pre></div>
+<pre><code>## SplitWeight Bootstrap Tree
+## ..||........................ 0.0171433 100 TRUE
+## ..||||...................... 0.0013902 14 FALSE
+## ..||||......|||||........... 0.0001589 0 FALSE
+## ||.........................| 0.0027691 1 FALSE
+## ||.......................... 0.0840367 100 TRUE
+## ...|||...................... 0.0001773 0 FALSE
+## ...|||........|.|........... 0.0003663 0 FALSE
+## |.|......................... 0.0060907 0 FALSE
+## ....||...................... 0.0385909 100 TRUE
+## ||||........................ 0.0018195 34 TRUE</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>col <span class="ot"><-</span> <span class="fu">rep</span>(<span class="st">"blue"</span>, <span class="fu">nrow</span>(tab))</span>
+<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>col[tab[,<span class="st">"Bootstrap"</span>]<span class="sc">==</span><span class="dv">0</span>] <span class="ot"><-</span> <span class="st">"green"</span></span>
+<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a>col[tab[,<span class="st">"SplitWeight"</span>]<span class="sc">==</span><span class="fl">1e-6</span>] <span class="ot"><-</span> <span class="st">"red"</span></span>
+<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb23-5"><a href="#cb23-5" aria-hidden="true" tabindex="-1"></a>pch <span class="ot"><-</span> <span class="fu">rep</span>(<span class="dv">19</span>, <span class="fu">nrow</span>(tab))</span>
+<span id="cb23-6"><a href="#cb23-6" aria-hidden="true" tabindex="-1"></a>pch[tab<span class="sc">$</span>Tree] <span class="ot"><-</span> <span class="dv">17</span></span>
+<span id="cb23-7"><a href="#cb23-7" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb23-8"><a href="#cb23-8" aria-hidden="true" tabindex="-1"></a><span class="fu">par</span>(<span class="at">mar=</span><span class="fu">c</span>(<span class="fl">5.1</span>, <span class="fl">4.1</span>, <span class="fl">4.1</span>, <span class="fl">8.1</span>), <span class="at">xpd=</span><span class="cn">TRUE</span>)</span>
+<span id="cb23-9"><a href="#cb23-9" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(tab[,<span class="st">"SplitWeight"</span>], tab[,<span class="st">"Bootstrap"</span>], <span class="at">log=</span><span class="st">"x"</span>, <span class="at">col=</span>col, <span class="at">pch=</span>pch, </span>
+<span id="cb23-10"><a href="#cb23-10" aria-hidden="true" tabindex="-1"></a> <span class="at">xlab=</span><span class="st">"Split weight (log scale)"</span>, <span class="at">ylab=</span><span class="st">"Bootstrap (%)"</span>)</span>
+<span id="cb23-11"><a href="#cb23-11" aria-hidden="true" tabindex="-1"></a><span class="fu">legend</span>(<span class="st">"topright"</span>, <span class="at">inset=</span><span class="fu">c</span>(<span class="sc">-</span><span class="fl">0.35</span>,<span class="dv">0</span>), <span class="fu">c</span>(<span class="st">"Pattern 1"</span>, <span class="st">"Pattern 2"</span>, <span class="st">"Pattern 3"</span>, </span>
+<span id="cb23-12"><a href="#cb23-12" aria-hidden="true" tabindex="-1"></a> <span class="st">"Pattern in the</span><span class="sc">\n</span><span class="st">best tree"</span>), <span class="at">pch=</span><span class="fu">c</span>(<span class="dv">19</span>,<span class="dv">19</span>,<span class="dv">19</span>,<span class="dv">17</span>), </span>
+<span id="cb23-13"><a href="#cb23-13" aria-hidden="true" tabindex="-1"></a> <span class="at">col=</span><span class="fu">c</span>(<span class="st">"blue"</span>, <span class="st">"green"</span>, <span class="st">"red"</span>, <span class="st">"black"</span>), <span class="at">bty=</span><span class="st">"n"</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,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" /><!-- --></p>
+</div>
+<div id="figure-4" class="section level3">
+<h3>Figure 4</h3>
+<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>YCh <span class="ot"><-</span> <span class="fu">read.tree</span>(<span class="fu">file.path</span>(fdir, <span class="st">"RAxML_bestTree.YCh"</span>)) </span>
+<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>mtG <span class="ot"><-</span> <span class="fu">read.tree</span>(<span class="fu">file.path</span>(fdir, <span class="st">"RAxML_bestTree.mtG"</span>)) </span>
+<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a>ncAI <span class="ot"><-</span> <span class="fu">read.tree</span>(<span class="fu">file.path</span>(fdir, <span class="st">"RAxML_bestTree.AIs"</span>)) </span>
+<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a>all_data <span class="ot"><-</span> <span class="fu">read.tree</span>(<span class="fu">file.path</span>(fdir, <span class="st">"RAxML_bestTree.3moles"</span>)) </span>
+<span id="cb24-5"><a href="#cb24-5" aria-hidden="true" tabindex="-1"></a>YCh_boot <span class="ot"><-</span> <span class="fu">read.tree</span>(<span class="fu">file.path</span>(fdir, <span class="st">"RAxML_bootstrap.YCh"</span>)) </span>
+<span id="cb24-6"><a href="#cb24-6" aria-hidden="true" tabindex="-1"></a>mtG_boot <span class="ot"><-</span> <span class="fu">read.tree</span>(<span class="fu">file.path</span>(fdir, <span class="st">"RAxML_bootstrap.mtG"</span>)) </span>
+<span id="cb24-7"><a href="#cb24-7" aria-hidden="true" tabindex="-1"></a>ncAI_boot <span class="ot"><-</span> <span class="fu">read.tree</span>(<span class="fu">file.path</span>(fdir, <span class="st">"RAxML_bootstrap.AIs"</span>)) </span>
+<span id="cb24-8"><a href="#cb24-8" aria-hidden="true" tabindex="-1"></a>all_data_boot <span class="ot"><-</span> <span class="fu">read.tree</span>(<span class="fu">file.path</span>(fdir, <span class="st">"RAxML_bootstrap.3moles"</span>)) </span></code></pre></div>
+<p>There are several option plotting a co-phylogeny. In the following we
+use the <code>cophylo</code> function of the <code>phytools</code>
+package.</p>
+<pre><code>library(phytools)
+par(mfrow=c(2,1))
+obj <- cophylo(YCh, mtG)
+plot(obj, mar=c(.1,.1,2,.1),scale.bar=c(.005,.05), ylim=c(-.2,1))
+title("A. YCh B. mtG")
+obj <- cophylo(ncAI, all_data)
+plot(obj, mar=c(.1,.1,2,.1),scale.bar=c(.005,.05), ylim=c(-.2,1))
+title("C. ncAI D. All data")</code></pre>
+<p><img 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" /> Unfortunately this function does not (yet)
+offer to add confidences to some splits, but we can do this easily with
+more basic plot functions.</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="fu">par</span>(<span class="at">mfrow=</span><span class="fu">c</span>(<span class="dv">2</span>,<span class="dv">2</span>), <span class="at">mar =</span> <span class="fu">c</span>(<span class="dv">2</span>,<span class="dv">2</span>,<span class="dv">4</span>,<span class="dv">2</span>))</span>
+<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>YCh <span class="ot"><-</span> <span class="fu">plotBS</span>(<span class="fu">midpoint</span>(YCh), YCh_boot, <span class="st">"phylogram"</span>, <span class="at">p=</span><span class="dv">0</span>, <span class="at">main =</span> <span class="st">"YCh"</span>)</span>
+<span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a>mtG <span class="ot"><-</span> <span class="fu">plotBS</span>(<span class="fu">midpoint</span>(mtG), mtG_boot, <span class="st">"phylogram"</span>, <span class="at">p=</span><span class="dv">0</span>, <span class="at">main =</span> <span class="st">"mtG"</span>)</span>
+<span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a>ncAI <span class="ot"><-</span> <span class="fu">plotBS</span>(<span class="fu">midpoint</span>(ncAI), ncAI_boot, <span class="st">"phylogram"</span>, <span class="at">p=</span><span class="dv">0</span>, <span class="at">main =</span> <span class="st">"ncAI"</span>)</span>
+<span id="cb26-5"><a href="#cb26-5" aria-hidden="true" tabindex="-1"></a>all_data <span class="ot"><-</span> <span class="fu">plotBS</span>(<span class="fu">midpoint</span>(all_data), all_data_boot, <span class="st">"phylogram"</span>, <span class="at">p=</span><span class="dv">0</span>, </span>
+<span id="cb26-6"><a href="#cb26-6" aria-hidden="true" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"All data"</span>)</span></code></pre></div>
+<p><img 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" /><!-- --></p>
+<p>We can compare this with consensus network with the different
+bootstrap values for the different genes.</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="fu">par</span>(<span class="at">mfrow=</span><span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">1</span>))</span>
+<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>cn <span class="ot"><-</span> <span class="fu">consensusNet</span>(<span class="fu">c</span>(YCh, mtG, ncAI))</span>
+<span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a>cn <span class="ot"><-</span> <span class="fu">addConfidences</span>(cn, YCh_boot) <span class="sc">|></span> <span class="fu">addConfidences</span>(mtG_boot, <span class="at">add=</span><span class="cn">TRUE</span>) <span class="sc">|></span> </span>
+<span id="cb27-4"><a href="#cb27-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">addConfidences</span>(ncAI_boot, <span class="at">add=</span><span class="cn">TRUE</span>) <span class="sc">|></span> <span class="fu">addConfidences</span>(all_data_boot, <span class="at">add=</span><span class="cn">TRUE</span>)</span>
+<span id="cb27-5"><a href="#cb27-5" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(cn, <span class="st">"2D"</span>, <span class="at">show.edge.label=</span><span class="cn">TRUE</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,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+<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="ref-Schliep2017" class="csl-entry">
+Schliep, Klaus, Alastair J. Potts, David A. Morrison, and Guido W.
+Grimm. 2017. <span>“Intertwining Phylogenetic Trees and
+Networks.”</span> <em>Methods in Ecology and Evolution</em> 8 (10):
+1212–20.
+</div>
+<div id="ref-Wang2012" class="csl-entry">
+Wang, Ning, Edward L. Braun, and Rebecca T. Kimball. 2012.
+<span>“Testing Hypotheses about the Sister Group of the Passeriformes
+Using an Independent 30-Locus Data Set.”</span> <em>Molecular Biology
+and Evolution</em> 29 (2): 737–50. <a href="https://doi.org/10.1093/molbev/msr230">https://doi.org/10.1093/molbev/msr230</a>.
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+
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+
+
+</head>
+
+<body>
+
+
+
+
+<h1 class="title toc-ignore">Maximum likelihood by hand</h1>
+<h4 class="author">Klaus Schliep, Iris Bardel-Kahr</h4>
+<address class="author_afil">
+Graz University of Technology, University of
+Graz<br><a class="author_email" href="mailto:#"><a href="mailto:klaus.schliep@gmail.com" class="email">klaus.schliep@gmail.com</a></a>
+</address>
+<h4 class="date">2023-01-27</h4>
+
+
+
+<div id="maximum-likelihood-by-hand" class="section level1">
+<h1>Maximum likelihood by hand</h1>
+<p>With the function <code>pml_bb</code> from <em>phangorn</em> <span class="citation">(Schliep 2011)</span> a lot of steps have become easier
+and shorter. If you want to have more control over all of the used
+parameters, it is also possible to use the older functions,
+e.g. <code>optim_pml</code>. The data is the same as in the vignette
+<em>Estimating phylogenetic trees with phangorn</em>:</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>(ape)</span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(phangorn)</span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>fdir <span class="ot"><-</span> <span class="fu">system.file</span>(<span class="st">"extdata/trees"</span>, <span class="at">package =</span> <span class="st">"phangorn"</span>)</span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>primates <span class="ot"><-</span> <span class="fu">read.phyDat</span>(<span class="fu">file.path</span>(fdir, <span class="st">"primates.dna"</span>),</span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a> <span class="at">format =</span> <span class="st">"interleaved"</span>)</span></code></pre></div>
+<p>As a starting tree, we calculate a neighbor joining tree:</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>dm <span class="ot"><-</span> <span class="fu">dist.ml</span>(primates)</span>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>treeNJ <span class="ot"><-</span> <span class="fu">NJ</span>(dm)</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" aria-hidden="true" tabindex="-1"></a>fit <span class="ot"><-</span> <span class="fu">pml</span>(treeNJ, <span class="at">data=</span>primates)</span>
+<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>fit</span></code></pre></div>
+<pre><code>## model: JC
+## loglikelihood: -3075
+## unconstrained loglikelihood: -1230
+##
+## Rate matrix:
+## a c g t
+## a 0 1 1 1
+## c 1 0 1 1
+## g 1 1 0 1
+## t 1 1 1 0
+##
+## Base frequencies:
+## a c g t
+## 0.25 0.25 0.25 0.25</code></pre>
+<p>The function <code>pml</code> returns an object of class
+<code>pml</code>. This object contains the data, the tree and many
+different parameters of the model like the likelihood. There are many
+generic functions for the class <code>pml</code> available, which allow
+the handling of these objects.</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="fu">methods</span>(<span class="at">class=</span><span class="st">"pml"</span>)</span></code></pre></div>
+<pre><code>## [1] AICc BIC anova logLik plot print simSeq update vcov
+## see '?methods' for accessing help and source code</code></pre>
+<p>The object fit just estimated the likelihood for the tree it got
+supplied, but the branch length are not optimized for the Jukes-Cantor
+<span class="citation">(Jukes and Cantor 1969)</span> model yet, which
+can be done with the function <code>optim.pml</code>.</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>fitJC <span class="ot"><-</span> <span class="fu">optim.pml</span>(fit, <span class="at">rearrangement=</span><span class="st">"NNI"</span>)</span></code></pre></div>
+<pre><code>## optimize edge weights: -3075 --> -3068
+## optimize edge weights: -3068 --> -3068
+## optimize topology: -3068 --> -3068 NNI moves: 1
+## optimize edge weights: -3068 --> -3068
+## optimize topology: -3068 --> -3068 NNI moves: 0</code></pre>
+<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="fu">logLik</span>(fitJC)</span></code></pre></div>
+<pre><code>## 'log Lik.' -3068 (df=25)</code></pre>
+<p>With the default values <code>pml</code> will estimate a Jukes-Cantor
+model. That means equal base frequencies and all transition rates are
+equal. The generic function <code>update</code> allows to change
+parameters manually. This is not what we usually want to do. However we
+might want to supply a different tree or change the number of rate
+categories.</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>fitF81 <span class="ot"><-</span> <span class="fu">update</span>(fitJC, <span class="at">k=</span><span class="dv">4</span>, <span class="at">inv=</span><span class="fl">0.2</span>, <span class="at">bf=</span><span class="fu">baseFreq</span>(primates))</span>
+<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>fitF81</span></code></pre></div>
+<pre><code>## model: F81+G(4)+I
+## loglikelihood: -3037
+## unconstrained loglikelihood: -1230
+## Proportion of invariant sites: 0.2
+## Discrete gamma model
+## Number of rate categories: 4
+## Shape parameter: 1
+##
+## Rate matrix:
+## a c g t
+## a 0 1 1 1
+## c 1 0 1 1
+## g 1 1 0 1
+## t 1 1 1 0
+##
+## Base frequencies:
+## a c g t
+## 0.37481 0.40160 0.03911 0.18448</code></pre>
+<p>In the line above we changed the model to a (discrete) rate across
+site model with 4 rate categories (using the default shape parameter of
+1), to 0.2 invariant sites and supply empirical base frequencies.</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>fitGTR <span class="ot"><-</span> <span class="fu">optim.pml</span>(fitF81, <span class="at">model=</span><span class="st">"GTR"</span>, <span class="at">optInv=</span><span class="cn">TRUE</span>, <span class="at">optGamma=</span><span class="cn">TRUE</span>,</span>
+<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a> <span class="at">rearrangement =</span> <span class="st">"NNI"</span>, <span class="at">control =</span> <span class="fu">pml.control</span>(<span class="at">trace =</span> <span class="dv">0</span>))</span>
+<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>fitGTR</span></code></pre></div>
+<pre><code>## model: GTR+G(4)+I
+## loglikelihood: -2611
+## unconstrained loglikelihood: -1230
+## Proportion of invariant sites: 0.006983
+## Discrete gamma model
+## Number of rate categories: 4
+## Shape parameter: 3.082
+##
+## Rate matrix:
+## a c g t
+## a 0.0000 0.968299 65.035522 0.8275
+## c 0.9683 0.000000 0.006153 25.1890
+## g 65.0355 0.006153 0.000000 1.0000
+## t 0.8275 25.188964 1.000000 0.0000
+##
+## Base frequencies:
+## a c g t
+## 0.37481 0.40160 0.03911 0.18448</code></pre>
+<p>We will change the model to the GTR + <span class="math inline">\(\Gamma(4)\)</span> + I model and then optimize all
+the parameters.</p>
+<p>With the control parameters the thresholds for the fitting process
+can be changed. Here we want just to suppress output during the fitting
+process. For larger trees the NNI rearrangements often get stuck in a
+local maximum. We added two stochastic algorithms to improve topology
+search. The first (set <code>rearrangement="stochastic"</code>) performs
+stochastic rearrangements similar as in <span class="citation">(Nguyen
+et al. 2015)</span>, which makes random NNI permutation to the tree,
+which than gets optimized to escape local optima. The second option
+(<code>rearrangement="ratchet"</code>) perform the likelihood ratchet
+<span class="citation">(Vos 2003)</span>.</p>
+<p>While these algorithms may find better trees they will also take more
+time.</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>fitGTR <span class="ot"><-</span> <span class="fu">optim.pml</span>(fitGTR, <span class="at">model=</span><span class="st">"GTR"</span>, <span class="at">optInv=</span><span class="cn">TRUE</span>, <span class="at">optGamma=</span><span class="cn">TRUE</span>,</span>
+<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a> <span class="at">rearrangement =</span> <span class="st">"stochastic"</span>, <span class="at">control =</span> <span class="fu">pml.control</span>(<span class="at">trace =</span> <span class="dv">0</span>))</span>
+<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>fitGTR</span></code></pre></div>
+<pre><code>## model: GTR+G(4)+I
+## loglikelihood: -2608
+## unconstrained loglikelihood: -1230
+## Proportion of invariant sites: 0.007396
+## Discrete gamma model
+## Number of rate categories: 4
+## Shape parameter: 2.993
+##
+## Rate matrix:
+## a c g t
+## a 0.0000 0.688144 70.761544 0.5744
+## c 0.6881 0.000000 0.004723 24.8257
+## g 70.7615 0.004723 0.000000 1.0000
+## t 0.5744 24.825736 1.000000 0.0000
+##
+## Base frequencies:
+## a c g t
+## 0.37481 0.40160 0.03911 0.18448</code></pre>
+<div id="model-comparison" class="section level2">
+<h2>Model comparison</h2>
+<p>We can compare nested models for the JC and GTR + <span class="math inline">\(\Gamma(4)\)</span> + I model using likelihood
+ratio statistic</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">anova</span>(fitJC, fitGTR)</span></code></pre></div>
+<pre><code>## Likelihood Ratio Test Table
+## Log lik. Df Df change Diff log lik. Pr(>|Chi|)
+## 1 -3068 25
+## 2 -2608 35 10 921 <2e-16 ***
+## ---
+## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
+<p>with the Shimodaira-Hasegawa test</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="fu">SH.test</span>(fitGTR, fitJC)</span></code></pre></div>
+<pre><code>## Trees ln L Diff ln L p-value
+## [1,] 1 -2608 0.0 0.5064
+## [2,] 2 -3068 460.5 0.0000</code></pre>
+<p>or with the AIC</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">AIC</span>(fitJC)</span></code></pre></div>
+<pre><code>## [1] 6187</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">AIC</span>(fitGTR)</span></code></pre></div>
+<pre><code>## [1] 5286</code></pre>
+<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><span class="fu">AICc</span>(fitGTR)</span></code></pre></div>
+<pre><code>## [1] 5298</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><span class="fu">BIC</span>(fitGTR)</span></code></pre></div>
+<pre><code>## [1] 5406</code></pre>
+</div>
+<div id="bootstrap" class="section level2">
+<h2>Bootstrap</h2>
+<p>At last we may want to apply standard bootstrap to test how well the
+edges of the tree are supported. This has already been shown in the
+vignette <em>Estimating phylogenetic trees with phangorn</em>.</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>bs <span class="ot"><-</span> <span class="fu">bootstrap.pml</span>(fitJC, <span class="at">bs=</span><span class="dv">100</span>, <span class="at">optNni=</span><span class="cn">TRUE</span>,</span>
+<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a> <span class="at">control =</span> <span class="fu">pml.control</span>(<span class="at">trace =</span> <span class="dv">0</span>))</span></code></pre></div>
+<p>Now we can plot the tree with the bootstrap support values on the
+edges and also look at <code>consensusNet</code> to identify potential
+conflict.</p>
+<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plotBS</span>(<span class="fu">midpoint</span>(fitJC<span class="sc">$</span>tree), bs, <span class="at">p =</span> <span class="dv">50</span>, <span class="at">type=</span><span class="st">"p"</span>)</span></code></pre></div>
+<div class="figure">
+<img 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" alt />
+<p class="caption">Tree with bootstrap support. Unrooted tree (midpoint
+rooted) with bootstrap support values.</p>
+</div>
+<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>cnet <span class="ot"><-</span> <span class="fu">consensusNet</span>(bs, <span class="at">p=</span><span class="fl">0.2</span>)</span>
+<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(cnet, <span class="at">show.edge.label=</span><span class="cn">TRUE</span>)</span></code></pre></div>
+<div class="figure">
+<img 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" alt />
+<p class="caption">ConsensusNet from the bootstrap sample.</p>
+</div>
+</div>
+</div>
+<div id="generating-trees" class="section level1">
+<h1>Generating trees</h1>
+<p><em>phangorn</em> has several functions to generate tree topologies,
+which may are interesting for simulation studies. <code>allTrees</code>
+computes all possible bifurcating tree topologies either rooted or
+unrooted for up to 10 taxa. One has to keep in mind that the number of
+trees is growing exponentially, use <code>howmanytrees</code> from
+<em>ape</em> as a reminder.</p>
+<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a>trees <span class="ot"><-</span> <span class="fu">allTrees</span>(<span class="dv">5</span>)</span>
+<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a><span class="fu">par</span>(<span class="at">mfrow=</span><span class="fu">c</span>(<span class="dv">3</span>,<span class="dv">5</span>), <span class="at">mar=</span><span class="fu">rep</span>(<span class="dv">0</span>,<span class="dv">4</span>))</span>
+<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span>(i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">15</span>)<span class="fu">plot</span>(trees[[i]], <span class="at">cex=</span><span class="dv">1</span>, <span class="at">type=</span><span class="st">"u"</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,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" /><!-- --></p>
+<p><code>nni</code> returns a list of all trees which are one nearest
+neighbor interchange away.</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><span class="fu">nni</span>(trees[[<span class="dv">1</span>]])</span></code></pre></div>
+<pre><code>## 4 phylogenetic trees</code></pre>
+<p><code>rNNI</code> and <code>rSPR</code> generate trees which are a
+defined number of NNI (nearest neighbor interchange) or SPR (subtree
+pruning and regrafting) away.</p>
+</div>
+<div id="session-info" class="section level1">
+<h1>Session info</h1>
+<pre><code>## R version 4.2.2 Patched (2022-11-10 r83330)
+## Platform: x86_64-pc-linux-gnu (64-bit)
+## Running under: Debian GNU/Linux bookworm/sid
+##
+## Matrix products: default
+## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0
+## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
+##
+## locale:
+## [1] C
+##
+## attached base packages:
+## [1] stats graphics grDevices utils datasets methods base
+##
+## other attached packages:
+## [1] phangorn_2.12.0.900 ape_5.6-2
+##
+## loaded via a namespace (and not attached):
+## [1] igraph_1.3.5 Rcpp_1.0.10 knitr_1.41 magrittr_2.0.3
+## [5] lattice_0.20-45 R6_2.5.1 quadprog_1.5-8 rlang_1.0.6
+## [9] fastmatch_1.1-3 fastmap_1.1.0 highr_0.10 stringr_1.5.0
+## [13] tools_4.2.2 parallel_4.2.2 grid_4.2.2 nlme_3.1-161
+## [17] xfun_0.36 cli_3.6.0 jquerylib_0.1.4 htmltools_0.5.4
+## [21] yaml_2.3.6 digest_0.6.31 lifecycle_1.0.3 Matrix_1.5-3
+## [25] codetools_0.2-18 sass_0.4.4 vctrs_0.5.1 glue_1.6.2
+## [29] cachem_1.0.6 evaluate_0.20 rmarkdown_2.20 stringi_1.7.12
+## [33] compiler_4.2.2 bslib_0.4.2 generics_0.1.3 jsonlite_1.8.4
+## [37] pkgconfig_2.0.3</code></pre>
+</div>
+<div id="references" class="section level1 unnumbered">
+<h1 class="unnumbered">References</h1>
+<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="ref-Jukes1969" class="csl-entry">
+Jukes, Thomas H., and Charles R. Cantor. 1969. <span>“{CHAPTER} 24 -
+Evolution of Protein Molecules.”</span> In <em>Mammalian Protein
+Metabolism</em>, edited by H. N. Munro, 21–132. Academic Press.
+</div>
+<div id="ref-Nguyen2015" class="csl-entry">
+Nguyen, Lam-Tung, Heiko A. Schmidt, Arndt von Haeseler, and Bui Quang
+Minh. 2015. <span>“IQ-TREE: A Fast and Effective Stochastic Algorithm
+for Estimating Maximum-Likelihood Phylogenies.”</span> <em>Molecular
+Biology and Evolution</em> 32 (1): 268–74. <a href="https://doi.org/10.1093/molbev/msu300">https://doi.org/10.1093/molbev/msu300</a>.
+</div>
+<div id="ref-Schliep2011" class="csl-entry">
+Schliep, Klaus Peter. 2011. <span>“Phangorn: Phylogenetic Analysis in
+<span>R</span>.”</span> <em>Bioinformatics</em> 27 (4): 592–93. <a href="https://doi.org/10.1093/bioinformatics/btq706">https://doi.org/10.1093/bioinformatics/btq706</a>.
+</div>
+<div id="ref-Vos2003" class="csl-entry">
+Vos, R. A. 2003. <span>“Accelerated Likelihood Surface Exploration: The
+Likelihood Ratchet.”</span> <em>Systematic Biology</em> 52 (3): 368–73.
+</div>
+</div>
+</div>
+
+
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+
+
+
+<h1 class="title toc-ignore">Phylogenetic trees from morphological
+data</h1>
+<h4 class="author">Iris Bardel-Kahr, Klaus Schliep</h4>
+<address class="author_afil">
+University of Graz, Graz University of
+Technology<br><a class="author_email" href="mailto:#"><a href="mailto:klaus.schliep@gmail.com" class="email">klaus.schliep@gmail.com</a></a>
+</address>
+<h4 class="date">2023-01-27</h4>
+
+
+
+<p>In this vignette, we will show how to work with morphological data in
+<em>phangorn</em> <span class="citation">(Schliep 2011)</span>. In most
+cases the different morphological characters or character states are
+encoded with the numbers 0:9 (or less, if there are less differences).
+Morphological data can come in different formats. The most common ones
+are <strong>.csv</strong> and <strong>.nexus</strong>.</p>
+<div id="load-packages" class="section level1">
+<h1>Load packages</h1>
+<p>We start by loading the <em>phangorn</em> package and setting a
+random seed:</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>(phangorn)</span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">9</span>)</span></code></pre></div>
+</div>
+<div id="load-data" class="section level1">
+<h1>Load data</h1>
+<p>The dataset we’re using contains morphological data for 12 mite
+species, with 79 encoded characters <span class="citation">(Schäffer et
+al. 2010)</span>. When reading in the <em>.csv</em> file,
+<code>row.names = 1</code> uses the first column (species) as row names.
+To get a <code>phyDat</code> object, we have to convert the dataframe
+into a matrix with <code>as.matrix</code>.</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>fdir <span class="ot"><-</span> <span class="fu">system.file</span>(<span class="st">"extdata"</span>, <span class="at">package =</span> <span class="st">"phangorn"</span>)</span>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>mm <span class="ot"><-</span> <span class="fu">read.csv</span>(<span class="fu">file.path</span>(fdir, <span class="st">"mites.csv"</span>), <span class="at">row.names =</span> <span class="dv">1</span>)</span>
+<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>mm_pd <span class="ot"><-</span> <span class="fu">phyDat</span>(<span class="fu">as.matrix</span>(mm), <span class="at">type =</span> <span class="st">"USER"</span>, <span class="at">levels =</span> <span class="dv">0</span><span class="sc">:</span><span class="dv">7</span>)</span></code></pre></div>
+<p>The data can then be written into a <em>nexus</em> file:</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">write.phyDat</span>(mm_pd, <span class="fu">file.path</span>(fdir, <span class="st">"mites.nex"</span>), <span class="at">format =</span> <span class="st">"nexus"</span>)</span></code></pre></div>
+<p>Reading in a <em>nexus</em> file is even easier than reading in a
+<em>csv</em> file:</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>mm_pd <span class="ot"><-</span> <span class="fu">read.phyDat</span>(<span class="fu">file.path</span>(fdir, <span class="st">"mites.nex"</span>), <span class="at">format =</span> <span class="st">"nexus"</span>, <span class="at">type =</span> <span class="st">"STANDARD"</span>)</span></code></pre></div>
+<p>After reading in the <em>nexus</em> file, we have the states 0:9, but
+the data only has the states 0:7. Here is one possibility to change the
+contrast matrix:</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>contrast <span class="ot"><-</span> <span class="fu">matrix</span>(<span class="at">data =</span> <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,</span>
+<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a> <span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,</span>
+<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a> <span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,</span>
+<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a> <span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,</span>
+<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a> <span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,</span>
+<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a> <span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,</span>
+<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a> <span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,</span>
+<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a> <span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,</span>
+<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a> <span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,</span>
+<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a> <span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>),</span>
+<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a> <span class="at">ncol =</span> <span class="dv">9</span>, <span class="at">byrow =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a><span class="fu">dimnames</span>(contrast) <span class="ot"><-</span> <span class="fu">list</span>(<span class="fu">c</span>(<span class="dv">0</span><span class="sc">:</span><span class="dv">7</span>,<span class="st">"-"</span>,<span class="st">"?"</span>),</span>
+<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">c</span>(<span class="dv">0</span><span class="sc">:</span><span class="dv">7</span>, <span class="st">"-"</span>))</span>
+<span id="cb5-14"><a href="#cb5-14" aria-hidden="true" tabindex="-1"></a>contrast</span></code></pre></div>
+<pre><code>## 0 1 2 3 4 5 6 7 -
+## 0 1 0 0 0 0 0 0 0 0
+## 1 0 1 0 0 0 0 0 0 0
+## 2 0 0 1 0 0 0 0 0 0
+## 3 0 0 0 1 0 0 0 0 0
+## 4 0 0 0 0 1 0 0 0 0
+## 5 0 0 0 0 0 1 0 0 0
+## 6 0 0 0 0 0 0 1 0 0
+## 7 0 0 0 0 0 0 0 1 0
+## - 0 0 0 0 0 0 0 0 1
+## ? 1 1 1 1 1 1 1 1 1</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>mm_pd <span class="ot"><-</span> <span class="fu">phyDat</span>(mm_pd, <span class="at">type=</span><span class="st">"USER"</span>, <span class="at">contrast=</span>contrast)</span></code></pre></div>
+<p>Now that we have our data, we can start the analyses.</p>
+</div>
+<div id="parsimony" class="section level1">
+<h1>Parsimony</h1>
+<p>For morphological data, one of the most frequently used approaches to
+conduct phylogenetic trees is maximum parsimony (MP).
+<code>pratchet</code> (as already described in <em>Estimating
+phylogenetic trees with phangorn</em>) implements the parsimony ratchet
+<span class="citation">(Nixon 1999)</span>. To create a starting tree,
+we can use the function <code>random.addition</code>:</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>mm_start <span class="ot"><-</span> <span class="fu">random.addition</span>(mm_pd)</span></code></pre></div>
+<p>This tree can then be given to <code>pratchet</code>:</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>mm_tree <span class="ot"><-</span> <span class="fu">pratchet</span>(mm_pd, <span class="at">start =</span> mm_start, <span class="at">minit =</span> <span class="dv">1000</span>, <span class="at">maxit =</span> <span class="dv">10000</span>,</span>
+<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a> <span class="at">all =</span> <span class="cn">TRUE</span>, <span class="at">trace =</span> <span class="dv">0</span>)</span>
+<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>mm_tree</span></code></pre></div>
+<pre><code>## 19 phylogenetic trees</code></pre>
+<p>With <code>all=TRUE</code> we get all (in this case 19) trees with
+lowest parsimony score in a <code>multiPhylo</code> object. Since we we
+did a minimum of 1000 iterations, we already have some edge support. Now
+we can assign the edge lengths.</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>mm_tree <span class="ot"><-</span> <span class="fu">acctran</span>(mm_tree, mm_pd)</span></code></pre></div>
+<div id="branch-and-bound" class="section level2">
+<h2>Branch and bound</h2>
+<p>In the case of our mites-dataset with 12 sequences, it’s also
+possible to use the branch and bound algorithm <span class="citation">(Hendy and Penny 1982)</span> to find all most
+parsimonious trees. With bigger datasets it is definitely recommended to
+use <code>pratchet</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>mm_bab <span class="ot"><-</span> <span class="fu">bab</span>(mm_pd, <span class="at">trace =</span> <span class="dv">0</span>)</span>
+<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>mm_bab</span></code></pre></div>
+<pre><code>## 37 phylogenetic trees</code></pre>
+</div>
+<div id="root-trees" class="section level2">
+<h2>Root trees</h2>
+<p>If we want our unrooted trees to be rooted, we have the possibility
+to use <code>midpoint</code> to perform midpoint rooting. Rooting the
+trees with a specific species (we chose <em>C. cymba</em> here) can be
+done with the function <code>root</code> from the <em>ape</em> package
+<span class="citation">(Paradis and Schliep 2019)</span>. To save the
+correct node labels (edge support), it’s important to set
+<code>edgelabel=TRUE</code>.</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>mm_tree_rooted <span class="ot"><-</span> <span class="fu">root</span>(mm_tree, <span class="at">outgroup =</span> <span class="st">"C._cymba"</span>, <span class="at">resolve.root =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a> <span class="at">edgelabel =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
+</div>
+<div id="plot-trees" class="section level2">
+<h2>Plot trees</h2>
+<p>With <code>plotBS</code>, we can either plot all of the trees with
+their respective edge support, or we can subset to only get a certain
+tree. It is also possible to save the plots as <em>.pdf</em> (or various
+other formats, e.g. svg, png, tiff) file. <code>digits</code> is an
+argument to determine the number of digits shown for the bootstrap
+values.</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"># plot all trees</span></span>
+<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plotBS</span>(mm_tree_rooted, <span class="at">digits =</span> <span class="dv">2</span>)</span>
+<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a><span class="co"># subsetting for tree nr. 9</span></span>
+<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a><span class="fu">plotBS</span>(mm_tree_rooted[[<span class="dv">9</span>]], <span class="at">digits =</span> <span class="dv">2</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"># save plot as pdf</span></span>
+<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a><span class="fu">pdf</span>(<span class="at">file =</span> <span class="st">"mm_rooted.pdf"</span>)</span>
+<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a><span class="fu">plotBS</span>(mm_tree_rooted, <span class="at">digits =</span> <span class="dv">2</span>)</span>
+<span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a><span class="fu">dev.off</span>()</span></code></pre></div>
+</div>
+<div id="consensus-tree" class="section level2">
+<h2>Consensus tree</h2>
+<p>To look at the consensus tree of our 19 trees from
+<code>pratchet</code>, or of our 37 most parsimonious trees from
+<code>bab</code>, we can use the <code>consensus</code> function from
+<em>ape</em>.</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="co"># unrooted pratchet tree</span></span>
+<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>mm_cons <span class="ot"><-</span> <span class="fu">consensus</span>(mm_tree)</span>
+<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a><span class="co"># rooted pratchet tree</span></span>
+<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a>mm_cons_root <span class="ot"><-</span> <span class="fu">consensus</span>(mm_tree_rooted, <span class="at">rooted =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a><span class="co"># branch and bound, we root the consensus tree in the same step</span></span>
+<span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a>mm_bab_cons <span class="ot"><-</span> <span class="fu">root</span>(<span class="fu">consensus</span>(mm_bab), <span class="at">outgroup =</span> <span class="st">"C._cymba"</span>,</span>
+<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a> <span class="at">resolve.root =</span> <span class="cn">TRUE</span>, <span class="at">edgelabel =</span> <span class="cn">TRUE</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" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mm_cons, <span class="at">main=</span><span class="st">"Unrooted pratchet consensus tree"</span>)</span>
+<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mm_cons_root, <span class="at">main=</span><span class="st">"Rooted pratchet consensus tree"</span>)</span>
+<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mm_bab_cons, <span class="at">main=</span><span class="st">"Rooted bab consensus tree"</span>)</span></code></pre></div>
+<div class="figure">
+<img src="data:image/png;base64,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" alt="Unrooted and rooted consensus trees of the mites dataset with MP." width="33%" /><img src="data:image/png;base64,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" alt="Unrooted and rooted consensus trees of the mites dataset with MP." width="33%" /><img src="data:image/png;base64,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alt="Unrooted and rooted consensus trees of the mites dataset with MP." width="33%" />
+<p class="caption">
+Unrooted and rooted consensus trees of the mites dataset with MP.
+</p>
+</div>
+<p>We can clearly see that, as expected, the two rooted trees have the
+same topology.</p>
+</div>
+</div>
+<div id="session-info" class="section level1">
+<h1>Session info</h1>
+<pre><code>## R version 4.2.2 Patched (2022-11-10 r83330)
+## Platform: x86_64-pc-linux-gnu (64-bit)
+## Running under: Debian GNU/Linux bookworm/sid
+##
+## Matrix products: default
+## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0
+## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
+##
+## locale:
+## [1] C
+##
+## attached base packages:
+## [1] stats graphics grDevices utils datasets methods base
+##
+## other attached packages:
+## [1] phangorn_2.12.0.900 ape_5.6-2
+##
+## loaded via a namespace (and not attached):
+## [1] igraph_1.3.5 Rcpp_1.0.10 knitr_1.41 magrittr_2.0.3
+## [5] lattice_0.20-45 R6_2.5.1 quadprog_1.5-8 rlang_1.0.6
+## [9] fastmatch_1.1-3 fastmap_1.1.0 highr_0.10 stringr_1.5.0
+## [13] tools_4.2.2 parallel_4.2.2 grid_4.2.2 nlme_3.1-161
+## [17] xfun_0.36 cli_3.6.0 jquerylib_0.1.4 htmltools_0.5.4
+## [21] yaml_2.3.6 digest_0.6.31 lifecycle_1.0.3 Matrix_1.5-3
+## [25] codetools_0.2-18 sass_0.4.4 vctrs_0.5.1 glue_1.6.2
+## [29] cachem_1.0.6 evaluate_0.20 rmarkdown_2.20 stringi_1.7.12
+## [33] compiler_4.2.2 bslib_0.4.2 generics_0.1.3 jsonlite_1.8.4
+## [37] pkgconfig_2.0.3</code></pre>
+</div>
+<div id="references" class="section level1 unnumbered">
+<h1 class="unnumbered">References</h1>
+<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="ref-Hendy1982" class="csl-entry">
+Hendy, M. D., and D. Penny. 1982. <span>“Branch and Bound Algorithms to
+Determine Minimal Evolutionary Trees.”</span> <em>Math. Biosc.</em> 59:
+277–90.
+</div>
+<div id="ref-Nixon1999" class="csl-entry">
+Nixon, K. 1999. <span>“The Parsimony Ratchet, a New Method for Rapid
+Rarsimony Analysis.”</span> <em>Cladistics</em> 15: 407–14.
+</div>
+<div id="ref-Paradis2018" class="csl-entry">
+Paradis, Emmanuel, and Klaus Schliep. 2019. <span>“Ape 5.0: An
+Environment for Modern Phylogenetics and Evolutionary Analyses in
+r.”</span> <em>Bioinformatics</em> 35 (3): 526–28. <a href="https://doi.org/10.1093/bioinformatics/bty633">https://doi.org/10.1093/bioinformatics/bty633</a>.
+</div>
+<div id="ref-schaffer2010phylogenetic" class="csl-entry">
+Schäffer, Sylvia, Tobias Pfingstl, Stephan Koblmüller, Kathrin A
+Winkler, Christian Sturmbauer, and Günther Krisper. 2010.
+<span>“Phylogenetic Analysis of European Scutovertex Mites (Acari,
+Oribatida, Scutoverticidae) Reveals Paraphyly and Cryptic Diversity: A
+Molecular Genetic and Morphological Approach.”</span> <em>Molecular
+Phylogenetics and Evolution</em> 55 (2): 677–88.
+</div>
+<div id="ref-Schliep2011" class="csl-entry">
+Schliep, Klaus Peter. 2011. <span>“Phangorn: Phylogenetic Analysis in
+<span>R</span>.”</span> <em>Bioinformatics</em> 27 (4): 592–93. <a href="https://doi.org/10.1093/bioinformatics/btq706">https://doi.org/10.1093/bioinformatics/btq706</a>.
+</div>
+</div>
+</div>
+
+
+
+<!-- code folding -->
+
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diff --git a/inst/doc/Networx.html b/inst/doc/Networx.html
new file mode 100644
index 0000000..1a709eb
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+<meta name="author" content="Klaus Schliep" />
+
+<meta name="date" content="2023-01-27" />
+
+<title>Splits and Networx</title>
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+
+<h1 class="title toc-ignore">Splits and Networx</h1>
+<h4 class="author">Klaus Schliep</h4>
+<h4 class="date">January 27, 2023</h4>
+
+
+
+<p>This tutorial gives a basic introduction for constructing
+phylogenetic networks and adding parameters to trees or networx objects
+using <a href="https://cran.r-project.org/package=phangorn">phangorn</a>
+<span class="citation">(K. P. Schliep 2011)</span> in R. Splits graphs
+or phylogenetic networks are a useful way to display conflicting data or
+to summarize different trees. Here, we present two popular networks,
+consensus networks <span class="citation">(Holland et al. 2004)</span>
+and Neighbor-Net <span class="citation">(Bryant and Moulton
+2004)</span>.<br />
+Trees or networks are often missing either edge weights or edge support
+values. We show how to improve a tree/networx object by adding support
+values or estimating edge weights using non-negative Least-Squares
+(nnls).</p>
+<p>We first load the phangorn package and a few data sets we use in this
+vignette.</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>(phangorn)</span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(Laurasiatherian)</span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(yeast)</span></code></pre></div>
+<div id="consensusnet" class="section level2">
+<h2>consensusNet</h2>
+<p>A consensusNet <span class="citation">(Holland et al. 2004)</span> is
+a generalization of a consensus tree. Instead of only representing
+splits (taxon bipartitions) occurring in at least 50% of the trees in a
+bootstrap or MCMC sample one can use a lower threshold and explore
+competing splits. Note that, in its basic implementation used here, the
+consensusNet edge lengths are proportional to the frequency of the
+corresponding splits in the provided list of trees.</p>
+<p>The input for <code>consensusNet</code> is a list of trees i.e. an
+object of class <code>multiPhylo</code>.</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">1</span>)</span>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>bs <span class="ot"><-</span> <span class="fu">bootstrap.phyDat</span>(yeast, <span class="at">FUN =</span> <span class="cf">function</span>(x)<span class="fu">nj</span>(<span class="fu">dist.hamming</span>(x)), </span>
+<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a> <span class="at">bs=</span><span class="dv">100</span>)</span>
+<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>tree <span class="ot"><-</span> <span class="fu">nj</span>(<span class="fu">dist.hamming</span>(yeast))</span>
+<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a><span class="fu">par</span>(<span class="st">"mar"</span> <span class="ot">=</span> <span class="fu">rep</span>(<span class="dv">1</span>, <span class="dv">4</span>))</span>
+<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a>tree <span class="ot"><-</span> <span class="fu">plotBS</span>(tree, bs, <span class="st">"phylogram"</span>)</span></code></pre></div>
+<p><img 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" /><!-- --></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>cnet <span class="ot"><-</span> <span class="fu">consensusNet</span>(bs, .<span class="dv">3</span>)</span>
+<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(cnet, <span class="at">show.edge.label=</span><span class="cn">TRUE</span>)</span></code></pre></div>
+<p><img 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" /><!-- --></p>
+<p>In many cases, <code>consensusNet</code> will return more than two
+incompatible (competing) splits. This cannot be plotted as a planar
+(2-dimensional) graph. Such as situation requires a n-dimensional graph,
+where the maximum number of dimensions equals the maximum number of
+incompatible splits. For example, if we have three alternative
+incompatible splits: (A,B)|(C,D) vs. (A,C)|(B,D) vs. (A,D)|(B,C), we
+need a 3-dimensional graph to show all three alternatives. A nice way to
+get still a good impression of the network is to plot it in 3D.</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="fu">plot</span>(cnet, <span class="st">"3D"</span>)</span>
+<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="co"># rotate 3d plot</span></span>
+<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a><span class="fu">play3d</span>(<span class="fu">spin3d</span>(<span class="at">axis=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>), <span class="at">rpm=</span><span class="dv">6</span>), <span class="at">duration=</span><span class="dv">10</span>)</span>
+<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a><span class="co"># create animated gif file </span></span>
+<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a><span class="fu">movie3d</span>(<span class="fu">spin3d</span>(<span class="at">axis=</span><span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>), <span class="at">rpm=</span><span class="dv">6</span>), <span class="at">duration=</span><span class="dv">10</span>)</span></code></pre></div>
+<p>which will result in a spinning graph similar to this</p>
+<div class="figure">
+<img src="data:image/gif;base64,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" alt />
+<p class="caption">rotatingNetworx</p>
+</div>
+</div>
+<div id="neighbornet" class="section level2">
+<h2>neighborNet</h2>
+<p>The function <code>neighborNet</code> implements the popular method
+of <span class="citation">Bryant and Moulton (2004)</span>. The
+Neighbor-Net algorithm is essentially a 2D-version of the Neighbor
+joining algorithm. The Neighbour-net is computed in two steps: the first
+computes a circular ordering of the taxa in the data set; the second
+step involves the estimation of edge weights using non-negative
+Least-Squares (nnls).</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>dm <span class="ot"><-</span> <span class="fu">dist.hamming</span>(yeast)</span>
+<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>nnet <span class="ot"><-</span> <span class="fu">neighborNet</span>(dm)</span>
+<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a><span class="fu">par</span>(<span class="st">"mar"</span> <span class="ot">=</span> <span class="fu">rep</span>(<span class="dv">1</span>, <span class="dv">4</span>))</span>
+<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(nnet)</span></code></pre></div>
+<p><img 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" /><!-- --></p>
+<p>The advantage of Neighbor-Net is that it returns a circular split
+system which can be always displayed in a planar (2D) graph. The
+rendering of the <code>networx</code> is done using the the fantastic
+igraph package <span class="citation">(Csardi and Nepusz
+2006)</span>.</p>
+</div>
+<div id="adding-support-values" class="section level2">
+<h2>Adding support values</h2>
+<p>We can use the generic function <code>addConfidences</code> to add
+(branch) support values from a tree, i.e. an object of class
+<code>phylo</code> to a <code>networx</code>, <code>splits</code> or
+<code>phylo</code> object. The Neighbor-Net object we computed above
+provides no support values. We can add the support values from the tree
+we computed to the splits (edges) shared by both objects.</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>nnet <span class="ot"><-</span> <span class="fu">addConfidences</span>(nnet, tree)</span>
+<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="fu">par</span>(<span class="st">"mar"</span> <span class="ot">=</span> <span class="fu">rep</span>(<span class="dv">1</span>, <span class="dv">4</span>))</span>
+<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(nnet, <span class="at">show.edge.label=</span><span class="cn">TRUE</span>)</span></code></pre></div>
+<p><img 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VORGzum0p7NGFR3fVL3ZH1XrtPkcx/zKBxva3CjStFqGO2IxuOb0FTZWWVfKqS+vX5op76rE3Q0Ho9HPH+cpS69D5F9aGwb328PvsT/4SuFaDTagQMHCKIuJ++16T+14diNjVPiw4cPe3l51bippjYv3Mjnh93YwQoriumKpN3LV2wn5/bs2fPIkSNcMxdHW5qiRI5r88usf8qUdaH+vb+/zA+eFRF54I/flg53e7JrfPCIyCwCIYRUsvhUhjjA5+Mdpu1f6i1VfalhtCOsy9rRR8h0CP4u8viZE7sW93HlCjuvTlFhrLi2oBVX0GrE2ujTZ0/u/n6AB9+m2+qkEoyx8u9vG3GDfntO7cdACv4YaMqrNIjt7ZiJpnQTOpNV5XNDo0VHR2OMsfrCeFsGjcYrnxPUp49SqSRnTXXhiL9Pre/LCrVS0/VJjXRJa55veJpRrKx+NLzAahjtCGOse3ZqSWhLGx6ba+4kDpsZKSs7QFFlxi4K83Yw55vbt+g0fPnRdPK7lGv6SqH6ob40w5XTeoms/KcoDn9hygna9FQx7TNHGp333sTodFrnlo6mnLd2rZZe3Uqe7exr9tbxaunX2hpUTdcnidc7e5vYfbVx/8L+vs4WplZuHUdvrPh0G5Ebv21qH7GzBZdtYukkCpkfk6nGGGtTlonYdmPjypstPD3B1cT3e1nDOMxsiIE1SLrMiI4c61F/lV0iwMSrbcEc3uCD8tI921+nf7Xj0Cud5qjStHOFryJDze2C52/6bcO0TuZM577/27Q1Wppn6A9i1nR9UvXPBAcG38zaY+Dy/X+fPrp5vL+Q6Tg6No/AGGvTfwkUCjwHL9sZc/J41LoxfuZ04dA/CzEmcneFcDkhu0o/KaBOXRHAbzLuVIF+N63WIDA9KYoeas7tvulZRQR5e8J47M6/PLgww5XTesnN1DVdzSzDNt/NEwpq+PbLxWfO/h5i5jj+tBxrbs734HjMu2Esr+bVfhuL9t7PbVgMxy8PPS890aSRLvFmmQ87LMdYl7FnXK/hG5PLrmU+3xLEtRoZK8cYq85OdOCUjqxIZO/pb2MVutP4PnFalU/o3aZBadNuJCldBvjZVuygzDoEtWXNWD12hUkWt3HG0j6H0rttvvyth/nAiEC78dcJXUHl+9MY3Ha+nldvShFCf3433jSVK1mmvvLXug27MuwCXfIunDlD/TZIJBJLS8tqF+G0GBWZ/PlK2YW4U3Fxp05G/3R8+8qlPdae+2taKw6Sy+LvsLqsXDnArvTUGrOpuytDVVSsQojnOvy3E8ORTvEmK/Pp44eJ+7dfx94rRRyEEMOxiROKyXquQy0V51cui3OZcXW4Y8M5OWfowj8NuqyNgRzhF9HFb01VZxyZG+zCoyGmuVfYguj75HuovD1hPHYHW7N3T2l6enoa5H9IOTs7u4KCDzo0q3x9EquvzXHndVqXWekDAHdW+nNcpl5QYyLn+uYJQS1teEyepXPLdr16SaxZ7nOukfuz4qhBfG7o7jz17Z/aCVwnxDWUo0OMMRwiGprm+tzmHI/5NyudYNFlrA/kMwRcBqIZ3et0enp6VVtSw/VJ8gyI6ReHK87EaO+tasez+/pEiUoaHmBq0XbavptPi7UYY1x48HNz888Pln5Nn/rqrGZc//BzuwfZWoftymowR4cYYzhENDDiZWL8Y57Ev2Wl54HuOm7riijf6ZdRFVcp6TQajc5gmlg0dnVvastTZMZfz+R6dfG2q/IyXv3o2bNn8+bNq5pbfn0ysOL4rfK3saiT41M06tbPcwjkSEcI6Z7sm7dG1mL8ph6sG3O2JTeZfH31MBETIYR0WTEHzitbzpXwyUdhODZxQpGHZi150GTGtWEORveqUz1DFw7eo2B/fxOG3ZADz9757EiPMZOLit79vGReZAhX8Pmhkvc+kv7UcH1Sm7LMh8Pl8RyCF/4e+/eRbfN7u3DN2y9PKCH34iyLLouOJaRKLx/dNPMzdzMOzaTH6pSc0lPziqPDhTTEaDrxTMP7qnQIzAjpHqxuzxYOP6rATGbFro3GsrysKn6amnQrq3JM6quz3U06rn3vR8D0q7rrk8SbyFAzn+9iDszo1syCZ9q4ZcdBc3aXXaDUZR2b1b25jcDU1t0vaPiSP6WnFkoseDbDDpZ+74rm5nwPllW/3c8Mv40fDAIzQrqMtR3YZgP/yCUkEkl5YI3aDdNo4hd4soRDDpZ/KlmdsSOske2AvRR/CsWgCv6Z4i5os/KW8Xzc8gPAezAjRHcODhUvWjxvxPfM4pLyqd3EVkxmiy4dG/0cOX/0Ct34NmZvbp387ccdz4J3nxtq91F+yTGh06qfn5w/eQftm+OTvRrm/1VDFw7eS50Rs2BA2+bMSn+BNmXKFIwxkZ+0c1pvbwdzLte0sVfQuF8vvfhYP9lHZG/uwaHRmLaBK64XNtQ9NA1jI/kcNniPgICA+Ph48t9Tpkz59ddfDbs++qV4cvPqQ7qbn6+LaYPdQTfM3S74JPCcA7o7G3ol6qiBXVQAoGGBwACgEAQGAIUgMAAoBIEBQCEIzIjggiubl/5xR/f+uVt2/Tlmm6yo0lUVeWrk5B6etgITYZMO78wCRgICMxr4zdkl4+aczmZXuuZDyF+W/7uDs/bApEFLLinJm/KU9YOCZ1xvMm7DwQM/fiavPAsYD7gOZngl6acO/HXh6smoqLOPrSb7OZe/6KkTnmdklS/WqnN7t7yTp06laTv7MtUJK4bPvR8Wc31zsBUNoUBu0vHQ0lkG2QZQBXg6DE735PKfx86/IDCPQ+OKA7zLnhKce+TnV2oBQoWlExj8siEQce6Rnzc/6hgeG2RF7u7YjY1qdERQDgIzOIbnN9uOfoM05yY3vXI9wLdsNDacezIqjjC1QvllgWHlmzxkYWVBx7kno+LUgb8OKt/ZEXmlswyxAaAa8IwYCV1mfMJrC1//pmV/lqy4cuq8km9mVrGIqvDBS6ZLU0eG4sqp80pJ9y6WZe/WcMHD0ln6Xm1QAwjMSJQk3bxNF7cRlQ3Zpr2XIJU78PkV4/7qcl7IsIef2JSc5edrX/FmTXaDnKXvtQY1gcCMgyblZpLazd/XomynpM188Bg143IqTinmPH32pklQkDuDnNW8WfnhvTbln/PkLH2vNqgJBGYUiGcJ8dmmvgHNy89wFL3JVdOFjErJ3L1f2GzgYAmrdJaleVl7JZd2Rz1qNnCwpOpR7oGhQGBGQZ50I5XmEyDmlk2gsbgcuu6lRluxzF2189xJYmbZrOel3x+jurPl+90FwXMnieGElRGCJ8UYVDnu75XHtuVTHDt2+tKeXj5r9aTvrSd3YCWsn708vdvmy1/aw2ulMYJnxQhUMTrijr0zTdQ55VN6epozK83yfrp94sBB06NpX+6/+MfopvBKaZxgyACj9mkPGfAxgD0YABSCwACgEAQGAIUgMAAoBIEBQCEIDAAKQWAAUAgCA4BCEJhRu3XrVvm/ExMTDbgmoHbgkxxGjU6nlz9BAoGgqKjIsOsDPhTswYxL5ZHbzp07V/nlr7i42MzJH0Zua1ggMGNSaeS2kpKS0NDQd+Y3Zz+AkdsaFvgQtlF4Z+Q2K0VJSEhISUnJO4ulZhWPtJHDyG0NCDwXxuCtkdu8fdz6h4ScP3/+38up1dosGhNGbmtAIDBjUDFym+uVq7e3jci4cZ6cYcqmFanfSubCC50HjNzWcMDTYTx0t6/efI5fZtw4R94OXzrcg/HuODZyHdY9OAEjtzUUEJixkMtzvvwliVBnkzfDw8Pn9nUtIRxZXP47S94+sOTm0VgYua1BgMCMglwuDw3pnfa69ItVwsPDFy5cqM18kIXcAwYMfWdhrVoVPHZ3BraBkduMHwRmeHK5PDQ09Oy5JPImWVf5yG2hkyaUL+lqQmeYCBFC+cVquSYp937paAIwcpvxwsCgSkpKunXrVv50hM1bXj4rb08Yj935l0c6Rtk7MR5f9MPpJGtr69KbZsIbN25grLy9pquZZdiuZzoDbgh4LwjMkN6pi2nd5aamYq4uY30gn+nU53+N7O3JBQKXxmowlslk1hYCcooJX7BiXCdnTuN+Ox9qqv5BwFAgMIN5d9/VlCn8Irr4rUXUGUfm9vF2YNBL92A3pFJyhkyWYGHGIyfSGayRP0YrDLAFoGYQmGG8U1d4eHg1C4tEInIxaVlgmNyPlR0rCoXksSIwOnCSwwBKz2qcPUveLDur8WF8fHzOnDlDNpafnx8cHHzz5s16XlFQZxCYvtVLXSRozPhBYHpVj3WRoDEjB4HpT73XRYLGjBkEpicU1UWCxowWBKYPlNZFgsaMEwRGOT3URYLGjBAERi291UWCxowNBEYhPddFgsaMCgRGFYPURYLGjAcERgkD1kWCxowEBFb/DF4XCRozBhBYPTOSukjQmMFBYPXJqOoiQWOGBYHVGyOsiwSNGRAEVj+Mti4SNGYoEFg9MPK6SNCYQUBgddUg6iJBY/oHgdVJA6qLBI3pGQRWew2uLhI0pk8QWC010LpI0JjeQGC10aDrIkFj+gGBfbCPoC4SNKYHENiH+WjqIkFjVIPAPsBHVhcJGqMUBPZffZR1kaAx6kBg/8lHXBcJGqMIBFazj74uEjRGBQisBp9IXSRorN5BYNX5pOoiQWP1CwKr0idYFwkaq0cQ2Pt9snWRoLH6AoG9xydeFwkaqxcQ2LugrnLQWN1BYG+But4BjdURBFYB6novaKwuDByY8vHpiIkh/s1sTblcs0ZNJb2nbE8swIZYE6irGtBY7RnwC9gVyRE9bJjC1p9/t3Fv9JHD+zYtHuwlYFiF/v5Up+c1KSkp6datW/nvJDw8XM8rUD2RSESumFQqNeBqyGQysjGEkFAovHHjhgFXpqEwXGC6h792FVgGb7ynqpimTlzUiskL3Z2rzxUx8rqw0QSGobEPZ7DAdFkbu3E43TdnE5UmEgWX1k+fuSOJbE73Jn7HzL6+LpZ8vmWTgKFrruSSyxK58dum9hE7W3DZJpZOopD5MZnq0gd9fWXDuJ6+bo1M+RYuAUNXX3pD4OoZf13YmALD0NgHMlhgxOs9fU3pJi2Hrf0r+aXy3/N1z2PHefK4bqHz1ked+GvXnC42TIdvThRirE3/JVAo8By8bGfMyeNR68b4mdOFQ/8sxBgrk34IMLUKGLfu0N9njm+f5G/KdJ1+SV3NOjSIurCRBYahsQ9hwPdgyju/f9VayKAhGkPg5Bfy7XcbDie+KO2ByD/+jSPLeej+RxpygvrS9CYcn6XJGl3GnnG9hm9MLlvw+ZYgrtXIWDnGqtPjGnMCfkzXkjNe/7Nm+ncHSm+9R0OpCxtfYBga+88MeZIDY0yUPEs68fvK6V909bBg0hDduse6VCXGxKvIMDOTwA2PK852aBVFBUXK8ttaec7TdOnlE9sn+ptyAjc81WGsPPmNLYPfcsjyvWfTXimqPzaUy+Vdu3Ytr+vnn3+mbhvrzggDw9DYf2PgwCpRZBwZ34pDtx59XIHlsSOtOB3WZvz7bCKRc33zhKCWNjwmz9K5ZbtevSTWLPc519QYY6x9Fhf+RTtnAYNGo/Od2n0VcbXKt2BbtmxpKHVhYw0MQ2P/gWECi1/Qgu046ew774/Ul2a4shqNjVPpnqzvyjEddqTirZniadLF6/fzldLwAFOLttP23XxarMUY48KDn5ubf36wsPLDKF/fOf/H8n5NWSzf8LQqjhDPnj1Lp9NpNNqqVavqedsoYLSBYWisJoYJLPV7Ecukx+a3rndpn/wx1JHTYvYVBSZe7+zNZXdd/6R0virt5w58i0F/vLgwrQmn9WKppuwuT3cPsOW0W/1Ah5Wnpvt49tt0vyyo4thRtpyAn6p+C4Zv3bqVnJxMxdbVO2MODENj1TJMYJrbqzoI6OY+w5ZtORATG3t474Ylo9o15tr1WBVfhDHGuqwdfYRMh+DvIo+fObFrcR9XrrDz6hSV5vrc5iyLLouOJaRKLx/dNPMzdzMOzaTH6pQclfrCVBcG233ImsNnL547unVusKtvRZAAAAi8SURBVDPXJmTHI31fsaaGkQeGobGqGeo9GJFzdcP4YB8XGwGbbSJ0aNl5yJytl7M15fN1z04tCW1pw2NzzZ3EYTMjZfkExliXdWxW9+Y2AlNbd7+g4Uv+lJ5aKLHg2Qw7mIeJ15dWD2/vZiPgsE2sm7UbvDDqdpGBtq3eGX9gGBqrAg1jg3z0D3wAsVgsk8kQQlKptDw2I5ScnNyjR4+cnByEkFAoPHXqVEBAgKFXysDg0/Sg3sBngv8NAgP1CRp7BwQG6hk0VhkEBuqfj4/P6dOnraysEEL5+fk9e/Z88OCBoVfKMCAwQAmRSHTmzBmysby8vBMnThh6jQwDAgNUEYlE//zzj6enp7e3d//+/Q29OobBNPQKgI+Zj4/P7du3Db0WhgR7MAAoBIEBQCEIDAAKQWAAUAgCA4BCEBiok+qGjsV5e/oKLEcclb97J831uc15bX66p9P32uofnKYHtadMWRfaY06C3YBJsyIW2Json0tjNkaMD37MlMWMcqQjbWqCTOc9W8x95274tTTxqYXEtwnDIGutVxAYqC0iY9u0xUmSX24em+jOJif1H9wGS9qsOXyuYNQICyIrMfG1Q1u/xu8eJqlk8Sk00Zc+LL2vsv7BISKoJeL53zHXNOJ+/d3YFROZbsHjJk/o14qPEFLI4tNYkgAvFkJInXlobGuhU5+18QVYdz9BWtzMV2xBQ0h1aoydSfDWF2V/lKg6NaaRSfDWlwbYHmpAYKCWaBwzU7bm2vrZ646nvFKVTTTrODlizddiNkLaO/EylVeARIAL4tf27zzmerttF4/M9DdHBdKE+yYiPw8mQrqMRGmBi6/EmkbeXZeZJCt0FousDLZV9Q0CA7VEsx7846/Dm2VHzQoR2Vs7+4eOWbTxSNJLTels/Eaa+NjaV0Q/Pq3bZz8rxx8/v2WwKxshpE2Nl+la+Yu4CKFiWUI6R+TbouydijwlKZ3lLfH8eN65QGCg1jgtRkUmP3uSeGLn8m87Cu5H/zRlgH+r3r/cUiGEkDo5PlnLTVkeOGiDzCRs7owOluReiniSmPS6sa/Eno6Q5la8TOvlLzYpfUDt7cQUTQuJj0lVP7HhgcBAndBM7MW9Rs2P2H/ubva9w+NbFp1dvvYfJUK6jISkXE3mfd7ErXNEb2L2xhWW3kEui09jivxasxAiniclPbeRSJxK/xfiHJn0ibmP+GM6uwiBgdpIWOjJcZp8TlN5Gte1z5dB9gwGi0VHqEiakM4PijgfM2/EmGG+RbGRx15jhBDS3kmQqlr4iwUIIVVyQirdx9+79IAQ51/8JwG18m3N/tePa7ggMFAbXB4X56anvyQqTdM9jd54KLvZiJGd2EhzK16mE4X2deMgetPBw9qp4yKjnxEI4ZykxCfmYt+mDIS09xJlJXbNm5mSx47K5E0RJ4ocxSIbmmG2iRIfz7tJoE8tBn0Z8PO8+SEjXk0M87HnEwWPZWf2bTuQ6bU89n/tuYh4nJT0wk4isqMjhOhOA4Z1mTd594GHY2Y7y+KTkc9gHzZCCCvkCt3T49v3DTD1Vicf+jF86y0l9zOJ58d1eczQAzOCmhnlwKPVDh1bEjPcgh+2u/QbEzHxandfM5b3/2TKtOUSrufCBHIxzcP9Y9s6mXLYgsbeYQv+PL+qK993eVVfJ9BAwcCjDUBDGXgU/Bu8BwOAQhAYABSCwACgEAQGAIUgMAAoBIEBQCEIDAAKQWAAUAgCA4BCEBgAFILAAKAQBAYAhSAwACgEgQFAIQgMAApBYABQCAIDgEIQGAAUgsAAoBAEBgCFIDAAKASBAUAhCAwACkFgAFAIAgOAQhAYABSCwACgEAQGAIUgMAAoBIEBQCEIDAAKQWAAUAgCA4BCEBgAFILAAKAQBAYAhSAwACgEgQFAIQgMAApBYABQCAIDgEIQGAAUgsAAoBAEBgCFIDAAKASBAUAhCAwACkFgAFAIAgOAQhAYABSCwACgEAQGAIUgMAAoBIEBQCEIrAFwdXVFCHE4HAcHB0OvC/gwNIyxodcB1CAnJ2fPnj0BAQEdOnQw9LqADwOBAUAhOEQEgEIQGAAUgsAAoBAEBgCFIDAAKASBGQWZTPb11197e3ubmpp6eHgsXbpUpVLVeK+5c+e2adNGp9MRBBEYGDhlyhQ9rCr4IBCY4W3durVt27aPHz+eNm3a9u3bu3fvvmLFiokTJ1Z/BQVjnJiYKJFIGAyGSqVKTU0NCAjQ2zqD/woDg4qKimKz2atWrdLpdOUTp06dyuFwHj58WM0dFQqFtbX1jh07MMZSqZTH46WlpVG+uuADwR7MkF69ejVt2rSvvvpq1qxZdHrFczFkyJA2bdrk5OQghBISEkJCQiwtLfl8vlgsPnr0KLnM/fv3i4uLxWIxQkgqlZqbmycnJ/v5+ZmZmXXq1CkxMdEgWwTeZejCP2k//PCDhYXFixcvqlogPT1dKBQOHjw4JiYmKirKz89PKBQWFhZijCMjIy0tLUtKSjDGEyZM4PP5Hh4e+/fvP3r0qL+/v6OjY15env62BFSBaejAP11arfbQoUO9evVq1KhRVcvcvHkzJCRk586dLBYLIZSfn79w4UImk4kQio+Pb9WqFZfL1el0SUlJFhYW58+ft7OzQwg5OzsHBAScO3euf//+etsc8F5wiGgwL1++vHv3brt27apZZvjw4Xv27NFqtffu3Tt58uT27du9vb05HA5BEElJSb6+vnQ6XS6X37lzZ8GCBWRdCKGmTZsyGIzi4mK9bAeoDgRmMNnZ2QRBWFtbvzNdq9WuWLEiOjoaY/zbb795eXmZmZkFBQWtX78+MzPTz8+PjCotLc3Pzw8hlJaWptFoevfuXfmRMcYuLi563R7wPvBpegAoBHswACgEgQFAIQgMAApBYABQCAIDgEIQGAAUgsAAoBAEBgCFIDAAKASBAUAhCAwACkFgAFAIAgOAQhAYABSCwACgEAQGAIUgMAAoBIEBQKH/A+XfucWpDtJCAAAAAElFTkSuQmCC" /><!-- --></p>
+<p>Analogously, we can also add support values to a tree:</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>tree2 <span class="ot"><-</span> <span class="fu">rNNI</span>(tree, <span class="dv">2</span>)</span>
+<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>tree2 <span class="ot"><-</span> <span class="fu">addConfidences</span>(tree2, tree)</span>
+<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="co"># several support values are missing</span></span>
+<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a><span class="fu">par</span>(<span class="st">"mar"</span> <span class="ot">=</span> <span class="fu">rep</span>(<span class="dv">1</span>, <span class="dv">4</span>))</span>
+<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(tree2, <span class="at">show.node.label=</span><span class="cn">TRUE</span>)</span></code></pre></div>
+<p><img 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H+kiCu5tnam7/P1OOFCjmFPXw8sWTPYb7G4w5L4nDcVJXlZtw5PaaPg8L3dGAgp7qdlKNt58SFfQAMaNWAWFhY5OTkaOfJevXq1gYpEuCRNlKusfJZXe9WQKDyyNDK99eSQAay7e3elO4Zt3zDW25ZLQ0iVH3coUdrBR8BFCBdLxPmGngJHqAILNKAZ3wdroCKRLCklXWVmlLVq0mLuvH6mRTf3RPz4u803ZxZ3YVGSWUwiP+n4+VS6nTT39omoyOhrxbSeqKpMjniZafeQe5A7s+EOANAwjRxPPlTPRQ4XFxdNHcEwxvVXJFJmRQjZbcPPJazwb2vKM7Bx9/18/q9pZTXXO1T5p+f2a2vO07dw8fIbv+yYOGGxwFjPfOyRMuWDNd7stvPvQhVYoBGNWr6obdu28fHxLi4umn3Hv/Vm/wi7mey9+QdHwWAKaE9LuIr4dxSZKWJlRx8BR9sdAbqthQaMyE9NfWUpFMLjvkC7mvFFjvpQHUIvlsxg6DG03RGg41powBCVqQdnh0D74BQKABJBwAAgEQQMABJBwAAgEQQMABJBwAAgEQQMABJBwAAgEQQMABJBwAAgkfYDhstvbl9x8IHqTwurMmPC+re34HGMHLsH75JU4H/UBEBTo+2A4ZIry0LCLxUwKe+XVWVsHe3/7R3HkG1HDq0dUHVo5uhl16UNNgHQBGntYd/K7IRDZ6/dOn/48JU80zAv+7qky0Wrx89/ODTuznZ/UwpCvuy0+MCEhCxlLyG9niZt7QYA9dLWJ1P17Max04kvCazHorD5Pu61/cClJ9dvf9pj1Rk/9WRQzFa2FpTqyipcXxMATZS2AkZr/+WuU18ixdUwp5t3fIQG6jNEXHr+8EW575bRdUc0oqykDBmbGlPradLGDgDwT5ASMJVK9fvvv3+8vLq6+s9TgKiepIiKjYXeTrVzpFXfTEiUClb0NqkdkuHyx49e0R2cbGnVSZ9sImMfANAEzQeMIAiZTLZu3bqPm8rKyiorKz9YUJmWfJ/Kn+5ZO0eaMkckrrIZILR+PyKT3JVg11C+fj1NGt8FADRF8wGjUqkcDufSpUsfN7Vt25bH+2CSJ0VGcpq8zQShce1BSfnkUR5ybutc1ytlxuXEEsfhfi405dlPNml8FwDQlEYav9Tc7JL/eeG7K9sP5Skfru9qpr6jhStKSuVUIxNDSu3NLl6PDQ/1nD1c6O+b1C+uvB57+KnzqCABOdNuaKIsreLaLHu93lueEQjhsn3DeCYTTlWR0lnQdDVKwGpvdn1wrwtVZWwd9cWxIorzuF0Ha+9oyRhsFlX1Ki9py2j/b+84frnoCxcqTV96cenK2qZC9TzYsgc7VsaW+8+fySdjECnN2BzoPXjlDa7/3MiYQwd/WTG+zbO9M/wnxOQTDb+4Dq6QWfhOndTPhoqQMlMkUbn78Nkk9BY0aRqfyvTDmX3f/XEhesOiqf2cuBSa/azENnUz+8pSlnRi23raMF0XJiswrkoItma2XyyS5W715dIM9OmWg1fvXhloz2o1/Je9X75votsNWb7//MUjPwTas1oN3/OYlPl3VY+39OGZ+EflyN4vk6d+15GuFxhb+h83mbupu57z3FvyhlcFLQupR7Cam12ZRbU3u+qu/5WeXL/9qXcPvdccgXcH+vs7WpTW0zfOaFNdSXt7de3SE5RxB5IOThPYq5tCdv82x/15dOio0d8cp4w7kHRwihMZh6+GytIipWhxB/2AbUkxYb5tTDk8iw7DNia/eZm0cbJvJ2sDroUw+NATFUJIdnG6FcdvRyFGCFVLUrIYAh83BkJI/uTo9E5GdkM2pfzbU07QHGk8sh/PTS+/MtOWJVx1X6mem554/dsIQ97QmOK6wnjya187sPgrM5X1NGm8o5/QUFla4lX0QDbD0K5b8Nb423fPrxlkRWNZ2bv1mb0r4c7t43O8uXTnOTflWPnHWh922/l3FBhjRcqi9npdf3yoIt4kbxxsa9gp+Eiu7OMtgxaoEcZgn7jZ1e9vb3Z9uon8jtagmAWt3TLeueDw3ABPazN778Dg76JOpr2qK0OmuJeaqTQfsunEjrDBXXwGfvWFD0PFC/zpZOS0AZ27DJs2vB0dURBCVRmpfzA6CdrTEcIl4tQ8M6EnNX523wHrpTPiE3cEtYbqLTpC45H96AhWfnCUvt6g3UWEurqKQry0I8tpzo33AxLppRBrliAiS6kQLzWjGWrrTxEeHl7XpU+VpcWqRxu6sh1mJaqPQIrU79zY7svT1aPBqjOTTDlD9r4mFMkLXdleq+8rMcbShGBLpkuv3vZMCtU6+EKlxv/koOki/whWc7PLu8GbXX5+LjTlk0cVlFZ9pobVdk+RvNCV5brgbqNcHVi/fn1drz9VlhZVZ6Q9oHn6uNfcHcBlktQnBt5dXGt2RvlQJK50FnoaotJ08TMDD74TDSFVriitVPHkoV7oznDPkrjfLr4l/W8OmgzSA0a8EKUU6At92tY9zvvpO1q4oqRURdFj150+kXyz62MNlaVFyuy0dGkbgYf64UlFpiid6CRUl+vDbyVpjzieQhe6IjM1E3cUdGIghCrEomyuX2Ri3IIJwWOFFWdiThfD1Q2dQXrAqtLuZlI8PrgDRPn0HS0Kg82iEBWV1fijpkbSQFlahN9kiJ9wPfhtaoaEqrxUcamdgG9eEzdlpiidcBN6sFXPxJJSGz7fgoqQ4l6KROUZOKwNC1GdgsZ2lV+MOf7i39xPA80Z2QFTZt1Nkzp4e1m8v8ls0N2vC+PWhpkrD1y4dDQiaODi7L4bIsdZU2uaHGm54mspHzc1jnajx/lQrywMmLBy5+FTZ8+e3B+1fErPzlMS20XsW96NjZDyXmoG4Sb0YNWsXp0uus9wF3aoOcASL8TiIks+35oqzUh9QHcXdGAgRBSkpb208vS0oiKEqHYjx/amXI899Fj16T6AFkXjI5k/XeRQ5Uf5soy+OP4OY/y+hKw89+T8Ie42hhwDa8+hi44/rH7/avmK4P6Opty/a2oc9ZWlxarcyB5sh6+vqYeEiuQFrmzvNQ/UtxAq48Ybc4fGlhIK0eIObP4P95QfLlRvvih2mAHDfbkEatTqhiZXQnbbtm3Z2dlbt27VbK8A0Ar4sSIAJIKAAUAiCBgAJIKAAUAiCBgAJIKAAUAiCBgAJIKAAUAiCBgAJGrUmX0rKip27txpZmZWzzq3bt1iMuHXiKCFaNSAjRw5EmNcVlZWzzrV1dUQMNBiNGrAoqKiGlyn5lnERugMAI0AxmAAkAgCBgCJIGAAkAgCBgCJIGAAkAgCBgCJIGAAkAgCBgCJIGAAkEh3A6aJGpYANKBRH5VqOqQZmwP7h4usRs6cG7nImiMtFMdFRc7wz6NL4ibb6u6XDtA4nQwYkbtr9tI0wU/Jp0Nd1M8VjwjqjAWdN564Wj55gvH/uXlVxVsZz4BDaXhN0OLp4rd1gzUsESJKRXvmDvdyNOXxTFt3/mLTrTL1yaMi//JPsz/v625jbGLnOXThsRx1WXPFzTnORkG7b/06a6C7g9/mRzD5PEAI6WbAKCwDfabi9tZ5m+MzimS1Cw16hEVunMpnIkS8PBvao9fM00T/eduPHt4UpHd5wWfhFyoQUuREj+48eFWy0ZDFuw7+Msvlj81fDJp/+R1CiHglkRRQUtZMjZb3nfdzVHCHRqsXCJo4jU/G/XEJ2X9lzpw55O3s+fPnMcYYSx/8OrGTEY2CKDSenVfAtCXbTqS+VE84T7yJ/9KWYT/mwFP19PHy6984sjxWpMue7hhoZNBtdVqVuqsVZyZZ0O1nJcoxro6fYkbldVuT/jc1Z4Eua3IBazSfqGFJFMUMNeD4bstT1a2prK4or5DKM773ZJqN2pXxqFb2+TluLMfZSXKszIoQMM0nnqrQ4v6AJkl3A/aB6tyTMzqyqGZT4qtx1ZlJpqzum3JVf1lH9WhD17/5oTXTd9tzFX5zYCRPf9TBN1rpPWjKdG0MpmyghiXx+mneO6ZD6/c1yaT54ut3H5UrHj54SOm28fFfgie7MtOWqriflqFs58XnNf7+gCZO1wJGaaCGJUWPy6HICvLV9TeR/P7WMT2HbRCpKHp6ekRhXr5c/Rqi4Oj0zsJpR15hhIsl4nxDT4EjXNkAH9H4MbGJnyIq7v/YnUc19Bj7/Y5DcWfOnPht27LJXVuxrfr/mFKBMcaq/N1DjOg2/kti4n8/t3fpkNZso14bMmQYV99e1JHN6zhh0/FLV87HrhzpyjXvuyGtEmMsvTDNku33SyGh5V0DTZDOBayBGpYYY9WLhGWBHcz1mGxDO/7QOTGSN+rkyJ6c+W6ou40h19C6Xc/xEaeya+p2Kh+s8Wa3nX8XalaCjzVqhUsAdI2ujcEAaFQQMABIBAEDgEQQMABIBAEDgEQQMABIBAEDgEQQMABIBAEDgEQQMABIBAEDgEQQMABIBAEDgEQQMABIBAEDgEQQMABIBAEDgEQQMABIBAEDgEQQMABIBAEDzRdRKjm4YmpgT3dHE30jG9fun684nStr8FWKO/Pb6nVel6NCiHi+zVfPflaiosEX/VcQMNBMVd/b+ZlXlyl78uwDZq/bFR35TT/69dWj+oWeeV3/PGm4WJz63FggdKQhJJOkZNL4Ph7klcnTyQJ8oNkjCo5MGzzrhtuqpP1zfExqDhOfB/kwBP13rP9tyZBvnD595JBJUjIonuM8GAgps0XiqnZjhAbkFUuEIxhofnDRsTmzjzEm7o6dW5suhBDidf180oDOxvLXKoRwmSh6doDAwUSPxTW15wcuOvW05jxQ9VAkfucs5BtTEH4jFj02dDNKXzbSy8HEwMyl59SfNV6nW+NTmTb5mX1Bc6fM/EHAMh6x7+UnJytXZv/ka8RrH/T9nrjz8Yc3B3sZUo3GHHuLMSZKYgK5JuPjKjHGsstf2dC4BmauoyIOXLh0avsMbyO67ZQzZZqcA71lBuzmzZtUKhycWxoKhRIbG4sV6cvdmaZjT5R/8v+/KndfyKDxUem1NRULd/ixTSedqcIYyxPD7Ni9fnqqwliZs74zg2Y77mihumSOQrzMnWE49kTVJzf877XMMRiLxeLz+SKRSNsdAZpHvPj5+h+oU7DXp6tFUVuP/+XceKSqLsl/8jzvceqB6DvYfY0nCyHiWWpacavOAmsqQhWSlAeM3mvWjLRSfxfTnVxa02QV72QI6Wmqt/A1D5oZouD5C4JqYmby18+u8uGJ1UtWHc9W4pK7v4QOcLPQN7Dz8BsXvvX4nSdSey+hJRWhKklKFt3TqxMDIUVWikQhHDrYpm47qoL8AmzZ2oGrwd5CwEAzQ2Hr6VFUhfmFxJ8W4+K45aErDheZWd5bM9hvsbjDkvicNxUleVm3Dk9po+Dwvd0YCCkfiMSydt58HkJEUVrqM7qtvdX7fOWePX3PuN9AL4YGewsBA80MzaVPbzsidX9MWvX7hbKHMaFzjrPGrF/UNXPvrnTHsO0bxnrbcmkIqfLjDiVKO/gIuAjh12mpzwz5QicaQvL0lAyF/GXha3VOVc/2L9goaTdjVn+OJnvbMsdgoCVj9/hmWeCRL9cM6v00ZLx/J8PizNuXTx66WOL9w6mtI6yoySwmkZ90/Hwq3U6ae/tEVGT0tWJaT1RVJkc8SUo68gjyYNZcra9gUu+snraUE9bL6GXirojNN52+uxzuqcnjF2qhl+lFIpFQKNRuHwCJiIr7hxcO49sZsRlsAyvXHp+F77pVqL5mqMo/PbdfW3OevoWLl9/4ZcfECYsFxnrmY4+UKbMiBOz2i0WKmqv1Bh5L4g5929fZWE+/VYceo8Nj60otalDLLMCXmpoaEhICVxGB1sEYDAASQcAAIBEEDAASQcAAIBEEDAASQcAAIBEEDAASQcAAIBEEDAASQcAAIBEEDAAS6VDAcPnN7SsOPlD9aWFVZkxY//YWPI6RY/fgXZIK/I+aAPiHdCZguOTKspDwSwXMD2boqsrYOtr/2zuOIduOHFo7oOrQzNHLrksbbALgn2v5vwerzE44dDBmEWoAAAnNSURBVPbarfOHD1/JMw3zsq/7SpGLVo+f/3Bo3J3t/qYUhHzZafGBCQlZyl5Cej1N2twV0Py0+A+M6tmNY6cTXxJYj0Vh833ca3cYl55cv/1pj1Vn/ExrjmnMVrYWlOrKKlxfEwD/TosPGK39l7tOfYkUV8Ocbt7xqZvDFZeeP3xR7rtldN0RjSgrKUPGpsbUepq0sQOgWSMlYARBpKamkrHlf+jBgwdyufyDBaonKaJiY6G3E029oPpmQqJUsKK3Se2QDJc/fvSK7uBkS6tO+mRT4+0BaCE0HzAWi8Xj8UJCQjS+5X+uqqqqtLT0gwWVacn3qfzpnkz1v5U5InGVzQCh9fsRmeSuBLuG8vXraWqs7oOWg5SAFRYWanyz/0rNlAHv/63ISE6Tt5kgNK49KCmfPMpDzm2d63ZfmXE5scRxuJ8LTXn2k02N1n/QYujEsIJ4IUop0Bf6tK27wlFRUiqnGpkY1gau8nrs4afOo4IEjHqatNBz0NzpRMCq0u5mUjx8+OzaBRQGm0VVvSosqpkTT/Zgx8rYcv/5M/n0+ppaPmnepcjQAG9nC30228DSSTB4VrTGq43oGF342Ciz7qZJHUZ6Wby/x2zQ3a8L49sNM1eahXVniLbOi8juu/3GOGtq/U0tmzRjc2D/cJHVyJlzIxdZc6SF4rioyBn+eXRJ3GTblr/7ZNH4RHBNwZ/mRVTlR/myjL44/u5Pq8hzT84f4m5jyDGw9hy66PjD6n/U1HKpHm/pwzPxj8qRvV8mT/2uI10vMLZUe91q9nQgYOAfUOVH9WWx+m0v+HDqTaL8+tZv5uxOq8mcqiRl95xhQgcTLtfE0WfMxpulNesSpSm7vh7CtzdmMzkmdp4BC+Oe1E4BWnxzW8hAYRtLfa6xg8+YDddLND+xZ1MHAQMYY0wU7xumT+V0GLvpbPor6cftqsIzIe312G0CF2w9fO7s3vDe5nSbL8+9ra/UnTTtBx99U5+QzUcv/B4fPdNbn976m+vyRt8zLdOFMZhadHR0WFgYh6PRuf2bv4kTJ27evJliFrR2y6Xcb/fPDTgYzrPl9/H3Hzh45OgAgSUDIYTLL3z31Z7KYTFJ+75woCOE+htKjva/mvxE6a+ffJ8dsO7QnlB3BkLIX/Amft9iFouOkPz6jp/T24dnRM1uS0MIe7DLGVdsLHRuLKf5qbObgr+dOvvkyZN79uyJjY3VVq+aJgMDAxpNfYsPVxVIrl1MuHgx4fyFGzllhGn/TVfPzu7ILI4d3uaritUPfp9Z+/iYSvquUsng8Vg1/1ZVlxQ+f573OPXA8m9389Y8+n2mreLCNPuAQ2ajFy0OGTGga3tzNnmFxps0bR9CSfG3p4gnTpwYMWKEVvrTDFXnnpzRkUU1mxJfjavOTDJldd+Uq/poLeL1ne1f+XUw16Prmdh36DpokMCM4RJ+W44xxsoXF1d90dWeR6NQqFy7rhMjb+ngEAzr3CEbfEy0uD3LLuyq4sNl7NZDxvlZ02gMBpV4/TTvHdOh9ftbFdJ88fW7j8plkk+XukOIZu23+MCtvNcv71/dv0j46uD8Wb/85deuugACBhBbj41Ls7NffVgyUvX8eNTRAucJk3oyKXpcDkVWkK++947k97eO6Tlsg0h695Ol7mQXv/XsMGL7IxVCLLN2vcd882UvYyqNrkMj/lo6uMvgr9qNHuezfsHCgAlFoUM9rLlEeZ7k9/27Dj1xizizvBsbIeaAUf04IWuDlxl87WtefC3q+x+zhBHbR5pWiT9V6o7KJt7knIv87ierEG/jij/O/xxxCPtFfd5GBx/n1PY5KilgDPYvEa9vbZvh7+FgzmMyOUY2HXp9Hr7zRoGirl31ImFZYAdzPSbb0I4/dE5MTam6T5e6w0Tx9Q3ju7Ux57GYHDPnrkGLD9+v0N7uaZFuXUXct2/fiRMntNUroINgDAYAiSBgAJAIAgYAiSBgAJAIAgYAiSBgAJAIAgYAiSBgAJCoZT4qJZVKX716tW7dug8XZmVllZSUaKtLQDe1zIDZ29vz+fyysrIPF7579+7P0/0CQLqW+ajU34JHpUDjgzEYACSCgAFAIggYACSCgAFAIggYACSCgAFAIggYACSCgAFAIggYACSCgIGG1VeYD5ftG8YzmXCq6q8vUtyZ31av87oc3Zts9EMt81lEoEENFOZTZookKvd578uHquFicepzY4HQUQfnQvwABAzUi8jdNXtpmuCn5NOhLsyaRSOCOmNB540nrpZPnmBM5KemFtt08Wr113MhmSQlg+I5zkPHS1vDKSKoD1F4Ie62gj98RBvm+4X0Nv4hYV8N78hFCFVLUrIYAh83BkJI/uTo9E5GdkM2pZRj1UOR+J2zkG9MQUiWEGzF8d/5svaxcllCsCXHf+crLexPo4OAgfpQWAb6TMXtrfM2x2cUyWoXGvQIi9w4lc9ESPkgRSJz8xHwcHnKphG9gu903ZV0co63ISoXix5yPL1c6QipclPF5Q5CgZm6gJHqSZrkrT3f01Rre9WIdOgUsbCwMD4+3sTERNsdaR4cHR3T0tLqL8yHcIk4Nc9M6EmNn9134hGjefGJi7ubUBBCyswUiarjPE82QuidRJTN8lzUrvazVpWRls1wD2+vE589Hfo9GELozZs3OrW//w8ul8tkqs8LP1GYj4VkF6c7BCa6dpXdSco3m3bu4U7/mvqhxONNPTtu63wpe1MPhuLmHNf+d8Oyrs9xoiKEkPLuArc+V6dIbi901YXrH9qdGh80Nx8W5sPK+6uEDAq91cC1e+cLWObjT5ar16o4NsaQN2L/G4yxKm9Lb7Zd2NXa6sxE4S9+bMtpF/6mEHRLBGMw8EkNFOZDqEIsyub6RSbGLZgQPFZYcSbmdDFGCCHlA5FY1s6bz0MIydJFmVQPb3f1CSF+k3RZhDoKOzE/ersWCQIGPqmBwnxIcS9FovIMHNaGhahOQWO7yi/GHH9BIIRfp6U+M+QLnWgIKXNSJZVWbZ31a65wSNN/jjxXYcv3NNeRks06MdAE/00DhfmIvLS0l1YCTysqQohqN3Js7wVhsYceB8+zl6SkI48gDyZCCFdXVauex0fvH6nvLk8/unbVzntS9gBBe525Pabtc1TQlNVbmK8ybrwxd2hsqbq0OVEUO8yA4b5cIs2KELDbLxbVrKZ4fGB6Fzt9FpPXyn3oomOJP/bhCiOylFraoUanW1cRAWhkMAYDgEQQMABIBAEDgEQQMABIBAEDgEQQMABIBAEDgEQQMABIBAEDgEQQMABIBAEDgEQQMABIBAEDgEQQMABIBAEDgEQQMABIBAEDgEQQMABIBAEDgEQQMABIBAEDgET/A0JOfBzlSnvDAAAAAElFTkSuQmCC" /><!-- --></p>
+</div>
+<div id="estimating-edge-weights-nnls" class="section level2">
+<h2>Estimating edge weights (nnls)</h2>
+<p>Consensus networks, on the other hand, provide primarily information
+about support values corresponding to a split, but no information about
+the actual difference between the taxon bipartitions defined by that
+split. For example, one may be interested how the alternative support
+values correspond with the actual genetic distance between the involved
+taxa. Given a distance matrix, we can estimate edge weights using
+non-negative Least-Squares, and plot them onto the consensusNet splits
+graph.</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>cnet <span class="ot"><-</span> <span class="fu">nnls.networx</span>(cnet, dm)</span>
+<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="fu">par</span>(<span class="st">"mar"</span> <span class="ot">=</span> <span class="fu">rep</span>(<span class="dv">1</span>, <span class="dv">4</span>))</span>
+<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(cnet, <span class="at">show.edge.label=</span><span class="cn">TRUE</span>)</span></code></pre></div>
+<p><img 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/><!-- --></p>
+<div id="import-and-export-networks-advanced-functions-for-networx-objects" class="section level3">
+<h3>Import and export networks, advanced functions for networx
+objects</h3>
+<p>The functions <code>read.nexus.networx</code> and
+<code>write.nexus.networx</code> can read and write nexus files for or
+from SplitsTree <span class="citation">(Huson and Bryant 2006)</span>.
+Check-out the new vignette IntertwiningTreesAndNetworks <span class="citation">(K. Schliep et al. 2017)</span> for additional
+functions, examples, and advanced application.</p>
+</div>
+</div>
+<div id="session-information" class="section level2">
+<h2>Session Information</h2>
+<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="fu">sessionInfo</span>()</span></code></pre></div>
+<pre><code>## R version 4.2.2 Patched (2022-11-10 r83330)
+## Platform: x86_64-pc-linux-gnu (64-bit)
+## Running under: Debian GNU/Linux bookworm/sid
+##
+## Matrix products: default
+## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0
+## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
+##
+## locale:
+## [1] C
+##
+## attached base packages:
+## [1] stats graphics grDevices utils datasets methods base
+##
+## other attached packages:
+## [1] phangorn_2.12.0.900 ape_5.6-2
+##
+## loaded via a namespace (and not attached):
+## [1] igraph_1.3.5 Rcpp_1.0.10 knitr_1.41 magrittr_2.0.3
+## [5] lattice_0.20-45 R6_2.5.1 quadprog_1.5-8 rlang_1.0.6
+## [9] fastmatch_1.1-3 fastmap_1.1.0 highr_0.10 stringr_1.5.0
+## [13] tools_4.2.2 parallel_4.2.2 grid_4.2.2 nlme_3.1-161
+## [17] xfun_0.36 cli_3.6.0 jquerylib_0.1.4 htmltools_0.5.4
+## [21] yaml_2.3.6 digest_0.6.31 lifecycle_1.0.3 Matrix_1.5-3
+## [25] codetools_0.2-18 sass_0.4.4 vctrs_0.5.1 glue_1.6.2
+## [29] cachem_1.0.6 evaluate_0.20 rmarkdown_2.20 stringi_1.7.12
+## [33] compiler_4.2.2 bslib_0.4.2 generics_0.1.3 jsonlite_1.8.4
+## [37] pkgconfig_2.0.3</code></pre>
+</div>
+<div id="references" class="section level2 unnumbered">
+<h2 class="unnumbered">References</h2>
+<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="ref-Bryant2004" class="csl-entry">
+Bryant, David, and Vincent Moulton. 2004.
+<span>“<span>Neighbor-Net</span>: An Agglomerative Method for the
+Construction of Phylogenetic Networks.”</span> <em>Molecular Biology and
+Evolution</em> 21 (2): 255–65. <a href="https://doi.org/10.1093/molbev/msh018">https://doi.org/10.1093/molbev/msh018</a>.
+</div>
+<div id="ref-Csardi2006" class="csl-entry">
+Csardi, Gabor, and Tamas Nepusz. 2006. <span>“The Igraph Software
+Package for Complex Network Research.”</span> <em>InterJournal</em>
+Complex Systems: 1695. <a href="https://igraph.org/">https://igraph.org/</a>.
+</div>
+<div id="ref-Holland2004" class="csl-entry">
+Holland, Barbara R., Katharina T. Huber, Vincent Moulton, and Peter J.
+Lockhart. 2004. <span>“Using Consensus Networks to Visualize
+Contradictory Evidence for Species Phylogeny.”</span> <em>Molecular
+Biology and Evolution</em> 21 (7): 1459–61. <a href="https://doi.org/10.1093/molbev/msh145">https://doi.org/10.1093/molbev/msh145</a>.
+</div>
+<div id="ref-Huson2006" class="csl-entry">
+Huson, D. H., and D. Bryant. 2006. <span>“Application of Phylogenetic
+Networks in Evolutionary Studies.”</span> <em>Molecular Biology and
+Evolution</em> 23 (2): 254–67.
+</div>
+<div id="ref-Schliep2011" class="csl-entry">
+Schliep, Klaus Peter. 2011. <span>“Phangorn: Phylogenetic Analysis in
+<span>R</span>.”</span> <em>Bioinformatics</em> 27 (4): 592–93. <a href="https://doi.org/10.1093/bioinformatics/btq706">https://doi.org/10.1093/bioinformatics/btq706</a>.
+</div>
+<div id="ref-Schliep2017" class="csl-entry">
+Schliep, Klaus, Alastair J. Potts, David A. Morrison, and Guido W.
+Grimm. 2017. <span>“Intertwining Phylogenetic Trees and
+Networks.”</span> <em>Methods in Ecology and Evolution</em> 8 (10):
+1212–20.
+</div>
+</div>
+</div>
+
+
+
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+
+
+
+<h1 class="title toc-ignore">Estimating phylogenetic trees with
+phangorn</h1>
+<h4 class="author">Klaus Schliep, Iris Bardel-Kahr</h4>
+<address class="author_afil">
+Graz University of Technology, University of
+Graz<br><a class="author_email" href="mailto:#"><a href="mailto:klaus.schliep@gmail.com" class="email">klaus.schliep@gmail.com</a></a>
+</address>
+<h4 class="date">2023-01-27</h4>
+
+
+
+<div id="introduction" class="section level1">
+<h1>Introduction</h1>
+<p>These notes should enable the user to estimate phylogenetic trees
+from alignment data with different methods using the
+<code>phangorn</code> package <span class="citation">(Schliep
+2011)</span> . Several functions of this <em>package</em> are also
+described in more detail in <span class="citation">(Paradis
+2012)</span>. For more theoretical background on all the methods see
+e.g. <span class="citation">(Felsenstein 2004; Yang 2006)</span>. This
+document illustrates some of the <em>package’s</em> features to estimate
+phylogenetic trees using different reconstruction methods.</p>
+</div>
+<div id="getting-started" class="section level1">
+<h1>Getting started</h1>
+<p>The first thing we have to do is to read in an alignment.
+Unfortunately there exist many different file formats that alignments
+can be stored in. In most cases, the function <code>read.phyDat</code>
+is used to read in an alignment. In the <em>ape</em> package <span class="citation">(Paradis and Schliep 2019)</span> and
+<em>phangorn</em>, there are several functions to read in alignments,
+depending on the format of the data set (“nexus”, “phylip”, “fasta”) and
+the kind of data (amino acid, nucleotides, morphological data). The
+function <code>read.phyDat</code> calls these other functions and
+transforms them into a <code>phyDat</code> object. For the specific
+parameter settings available look in the help files of the function
+<code>read.dna</code> (for phylip, fasta, clustal format),
+<code>read.nexus.data</code> for nexus files. For amino acid data
+additional <code>read.aa</code> is called. Morphological data will be
+shown later in the vignette <em>Phylogenetic trees from morphological
+data</em>.</p>
+<p>We start our analysis loading the <em>phangorn</em> package and then
+reading in an alignment.</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>(ape)</span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(phangorn)</span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>fdir <span class="ot"><-</span> <span class="fu">system.file</span>(<span class="st">"extdata/trees"</span>, <span class="at">package =</span> <span class="st">"phangorn"</span>)</span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>primates <span class="ot"><-</span> <span class="fu">read.phyDat</span>(<span class="fu">file.path</span>(fdir, <span class="st">"primates.dna"</span>),</span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a> <span class="at">format =</span> <span class="st">"interleaved"</span>)</span></code></pre></div>
+</div>
+<div id="distance-based-methods" class="section level1">
+<h1>Distance based methods</h1>
+<p>After reading in the nucleotide alignment we can build a first tree
+with distance based methods. The function <code>dist.dna</code> from the
+<em>ape</em> package computes distances for many DNA substitution
+models, but to use the function <code>dist.dna</code>, we have to
+transform the data to class DNAbin. The function <code>dist.ml</code>
+from <em>phangorn</em> offers the substitution models “JC69” and “F81”
+for DNA, and also common substitution models for amino acids
+(e.g. “WAG”, “JTT”, “LG”, “Dayhoff”, “cpREV”, “mtmam”, “mtArt”, “MtZoa”
+or “mtREV24”).</p>
+<p>After constructing a distance matrix, we reconstruct a rooted tree
+with UPGMA and alternatively an unrooted tree using Neighbor Joining
+<span class="citation">(Saitou and Nei 1987; Studier and Keppler
+1988)</span>. More distance methods like <code>fastme</code> are
+available in the <em>ape</em> package.</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>dm <span class="ot"><-</span> <span class="fu">dist.ml</span>(primates)</span>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>treeUPGMA <span class="ot"><-</span> <span class="fu">upgma</span>(dm)</span>
+<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>treeNJ <span class="ot"><-</span> <span class="fu">NJ</span>(dm)</span></code></pre></div>
+<p>We can plot the trees <code>treeUPGMA</code> and <code>treeNJ</code>
+with the commands:</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">plot</span>(treeUPGMA, <span class="at">main=</span><span class="st">"UPGMA"</span>)</span></code></pre></div>
+<div class="figure">
+<img 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TG6gv/9mMZT4/zsmn+Xgaeuwl/Dc4AAAMCKTAlxWoNnp/krhwTzichc8vDm8ZilG0f3ryaLXxD5P12k2Hx9tRZDB7fyxu/48NcQgAAAwHosGSr1PWFkr1F9enkwZcv6hj8+03RV0k09/W8BiHFrO2tL23+wSKiKEI8BAMB6irRxV9mg6EhX5uki9smtlAeC8EipbenXJamHv3i/ucTf1d6heljnzw+kGomIinb3c7RpsDjJXL6bMX5+pNhv+OHckmMjq4vbrM1mich48bN6Tt3WXtg+tVeTUG9nx2rhvZZdymPLd8k6/u2Efi0l3q5uvrKun+9OLqq09w1WhwAEAABWY7qm1BS5RjSoW/pXciz6xzdPrBg582TdT5aOqc8nosK4L1speq5/oBi+5OfYdWPrJK7o3+/rBBORjX+gN5OdlV02z4fN3jHn25RmM2Z1cEzX6J74KmSeDBGbo9Nk6E/OHPKToNucDXu3T4/MOTBj+s+3LURkTI7pHd1xwWWXTtPXb/9hXN2kFf07fHb8/zHzCN4uaIFBpTIYDHPmzLGxsbF2IQBgZbVr1x4xYniOWpVmun+9g92zv6vMCAMH7Tgzv6kTQ2TSLRu18Oa76+JiB/sJiKhTY2FCwIC9B5Onhgf7BfozD29nFxMJiYrOLJl/1HvsmQ/9eUWxqiRh+ORgAREZElQJJlvZtL37J4fYEJFifKflJ64WFrNkSd80YcrJgLkn/5gmtyOijk3Eiad7HDqgWt6qudBKHwlUKgQgqFTDhg2rVq0awzD/eVMAqNJcXFyIDNo4nSXgvWVLevuVNiSMebeOr1m0aegHitA/xgeZTq9bnxg2aesAv/KLlbh6DWfmmsHIEuMaEOBq0WXdNZMT3Vg3a2Nxv62TFSIyxavjDUGDJfZEZE7T6HI9ek4ZGVL6Sxf7KCOjwDlM4sc3X92y5oSgzerOTndSUoiIyCxw9+TzeOiLcAYCEFSq1atXW7sEAHhjmK+qNI/tGvQe0aeXw9OFvcMfn2u47Ldzj8fVTj12/H7dfh3q8J+uLM7MyBEE1vXjEzF+gX702+27Ftbj8Pyv46Nnbu7kyhD7SKfJcJLKA/lEVBSvShI2ntS0/ODGeFUCSXpJbCxpx35LNORoR0j2PF+PTYvRtfgE3ICsCwAAVpKvVV5nQiIkdhWWms1msnVxETNFyUnp5Bvg8yyT5P7+y2k2um0zZ4aI71PLX5hz+06eesWcvW6jFw6txSMiY4IqgQ1ThAuJyJSkjjcERUjL84/5llqT66uQezKmG9duMI2WppjZCvQnxvjgssgV+JcGAADrMCbEaQ3uUrn/c6Mu7JOTP+695diyUxM7Yvh8viX7dvk8Z8o/t3DePmG30X19eUREIv9aNdmsi0tnrH7Yc/6nkSIiInOGRvvIWy6vxqPSwSDHssEgIqJCnTLJRhoZLCDGzs7Okp2eZShbY7kTOzI6YviueywBV6AFBgAA1nFbpb7DupnTjhw4wBARscbcG7+uXLglt+W3C/pWY4ht3r2N05AVo6Z4z+zmV6jZtnDe1uJ+277uWfbAIL5/oJ9lxZqloiZLtV3dSpeVxKuuCSTjQoRUNhgU3lVSPqfZmKjUGYNHSMVE/OgPBgTFrBwzMmDeIJk4++z6BUvOeM872tkL8xO5AwEIAACsQ6u8YjQ9jhnRI4aIiBi+yNk7rMWobT/N7BssJCKmxoCYw3mfTfl2au8f9M613+k6/4+5o5t4lbcuGLeAAGfTKY9JC4YHli0zJani9XX7Sx2ofDCosaxa2Tr2gUaT5SpTBPCJyPaduYdi+RNmLB3avcDeV9Jy4LazE7vWE1fyBwDWxLAsBvzeAvPmzRMIBALBGxRYTSbTvHnzDAbDf94UAADgDfMGXVDhb6xbt65Dhw4eHh7WLuQZk8mE9AwAAG8pjAC9HcLCwnbu3BkaGmrtQp4pKSlxdXUtLi62diEAAACvDXeBAQAAAOcgAAEAAADnIAABAAAA5yAAAQAAAOcgAAEAAADnIAABAAAA5yAAAQAAAOcgAAEAAADnIAABAAAA5yAAAQAAAOcgAAEAAADnIAABAAAA5yAAAQAAAOcgAAEAgNUUHRjoxmNeSRg8XWn6/xzbeGqcn13z7zIs/1SxUKUIrF0AAABwFxsybENsdwsRUdHpJcNXP+z1zeJevjwiIn71SMn/5xrF5uurtRg6uJU3ftOHV0EAAgAAq7Gv+26PukREZLocN4N1ath3ZJ/u4v96d3N+nt7BScy8ah3j1nbWlravU4w5P4/v6PQ6e8BbDMEYAACsj32gUaUxYVEy2/IFj5UxEzor/N3sRPbufvIu0w6kGYmIyHhucm2XPhvObxrXXuLfZsVNU8751aM6RNat7uTgFhDdf+nZRywRkf7YyOriNmuz2dLDGbOOfzuhX0uJt6ubr6zr57uTi+hVR6vstw3WgwAEAADWZ9DFxZtrKhRlDStz8spebSb9Lu4+d9O+fZtmtxec+erDaQfzichyT6u9w8QtGhpjaPnp96sH6Zd0aj9H4z9w0ZZ9u77uzB7+fND8c0Yic5pG98RXIfNkiMiYHNM7uuOCyy6dpq/f/sO4ukkr+nf47HjBy0cbYc2PACoXWmDwv2NZ9tatW9auAgDeSm5ubi4uLuVfmW+qNI9FsqhwIRERWTIuJ9p2XrJj48cSIRG1U+Qe/mm6SCQgIkO8KsFgqjd814nPJSIy/DGqiy54SvzqCfX4RKzU9onwhHc1HlFRvCpJGD45WEBkSd80YcrJgLkn/5gmtyOijk3Eiad7HDqgWt6qubnC0YBLEIDgf8Tn84VCYZs2baxdCAC8laKionbs2FH+Vb5WeZ3q91I4ln7JqzXghyMDyFz8MCs1Mz1FtS3mIitZJBMRmW9pdHnuPaeOLc0rrMlkNl39cfZin496tG0Y7Nly8vKWRESmJHW8IWiwxJ7IfHXLmhOCNqs7O91JSSEiIrPA3ZPP4/FeOhpwCQIQ/I+EQmF+fr61qwCAKsGYEKfVu7WJDOQTERH78NLaWbNW7j6bXGBXs1ZQqL8+tcRvSIQXj+iJTp0sajaluUPpjqI2X/z8RdGctYsH75rFin2ie01eunxCQzd6pNNkOEnlgXyypBz7LdGQox0h2fP8GW1ajK7Fp4KKRwMuwRwgAACwMstttfoOXxoltSEiMmgXdWwzXRMy43Bybv7D9Kvndw6pYxTLo0KFRMZEdbypfqT8aWLh12wzfdv59Jy7iX9unRZxb/tn4364ZiZjgiqBDVOEC4lMN67dYBotTTGzFehPjPHhvXQ04BAEIAAAsLISbdwVNjBS4c4QkfHS5vW6gLFrvnk/yseeT2TO2r/jZElIA4U9EftAq8lylikC+ERE+mOTZCE91tw0E4k86jd/b+KwZq48vkBA5gyN9pG3XF6NR8TY2dlZstOzDGXnstyJHRkdMXzXPfaFowHHoAUGAADWZUpSagrs5ZH1BUREjI3IxpJ1es+vKoFvya0Le1cvjzn1gN+Uih4byCFBfYUkfSQ2RETEs7XkJh9ZPvPb6h9FueYn/fr9lzvYNqv71eGX7FddE0jGhQiJSBj9wYCgmJVjRgbMGyQTZ59dv2DJGe95Rzt7MaSvcDTgGIwAAQCAVbGPNKpbTFiU3I6IiARR478Zr8he+36LVn0nrjrvMHJX7Cdy0bkli08UmVM1uid+crlH6ZMPhU1n/bykr6dq8aAOrTsNW3LGacCWU9uH+vNMSap4fV2FtLSzZfvO3EOxEwN1S4d27zFs4e/igdvOHvxELiZ64WjAMQzLstauAf6zsLCwnTt3hoaGWrsQAACAqgAjQAAAAMA5CEAAAADAOQhAAAAAwDkIQAAAAMA5CEAAAADAOQhAAAAAwDkIQAAAAMA5CEAAAADAOQhAAAAAwDkIQAAAAMA5CEAAAADAOQhAAAAAwDkIQAAAAMA5CEAAAADAOQhAAAAAwDkIQAAAAMA5CEAAAGBNRQcHudkETb1sqrDUfGW+3Map9848K1UFVR4CEAAAWJHpulJTII6IDhE8v5TNVV66TsFRMntr1QVVHQIQAABYD/tYo7zJSKIV4gqLjdrLGqOHPKIW30p1QZWHAAQAANZjjFfqzD6Rkd4VLkfmW3HKBwJplFRIRPqjI6qL2627y5at1B8d4SVut+4eS2RSTg9x7Lzq9JaxLeq4ix2qhXRbejn37umlH7YIr+lkXy1ixI5UMxERsY+VMRM6K/zd7ET27n7yLtMOpBlLz3/xs3pO3dZe2D61V5NQb2fHauG9ll3KYwmqPAQgAACwGnO6Wp1jq4gOF1ZYnK+6lMjWjlS4MkTmWyrNE/8IhQdTtkuqWpvnJ5e5M8Q+0mlS9We/mnau/pSff907M/Lx4RndpK3nZDaZFrN386iA65tnrrpkJDInr+zVZtLv4u5zN+3bt2l2e8GZrz6cdjCfiNgcnSZDf3LmkJ8E3eZs2Lt9emTOgRnTf75tqfyPAiqZ4D9vAgDAPRcuXBg/fnytWrWsXUiVJZPJpk+fXqRVXjPmX3rPmXnvhfWM64DIIAERFWiV10WyafXLr1dF8errQsmUYAGR4YoqweTZadnetb29GKLg/g3m/Z7Y5dt9y1u5MGR26l7/+40MEVkyLifadl6yY+PHEiERtVPkHv5pukgkICJDgirBZCubtnf/5BAbIlKM77T8xNXCYgwBVX0IQAAAr5CTk2M0Gvv06WPtQqqsmjVrEpkS4zTFHp3mrxoS/NxkH0v2gRmTdtaIktkSkfFKnNYUOlZePkfIlKiKN9YfIhUTWTI12ofevUZ29WKIiEwpySkU8sHI5i4MEZEhJTmNVz88SEC8WgN+ODKAzMUPs1Iz01NU22IuspJFMhGROU2jy/XoOWVkiA0REbGPMjIKnMMkfph6VPUhAAEAvIJIJPLy8kIA+nexd9TqDJ58yog+vbyYZ4uLfzk0mrw7KWryiCzZanW2p0LhWzZjg83RajKcpfIAPlFhvPoaX1Y6rEPEPtaqUp2i3gkqvbCZbig1hbW7y5wZ9uGltbNmrdx9NrnArmatoFB/fWqJ35AILx5RfrwqSdh4UlOHsjMb41UJJOklsanEDwGsBHOAAADASvQ6ZbyllkLmzjy/1HxDpc0TyaLChKWbJPCkUZKyX9fZ3NPHlRQWEW5DZLqu1pXUUUidSvc2Jih1lvCIsvDC5mnVN8WyiLoW7aKObaZrQmYcTs7Nf5h+9fzOIXWMYnlUqJDIlKSONwRFSMvzj/mWWpPrq5B7VqgHqiYEIAAAsA7zTaUm104SEVzxEUB5OvUNqh8pdyQiU7JKW1i9Xm3H0khSovt++ZF8H7nMkyE2N16Tai+V1yltV5nTVZpHz8KLKUGps4RGSPmXNq/XBYxd8837UT72fCJz1v4dJ0tCGijsidhHOk2Go1QeWN7wKtQpk2ykkcFojnABAhAAAFgFm6dRXmdCIqV2FRabEpQ6g5siMpBPRGxxUbE583DM1nPx6j9/mtbtvWVXSmwlimAhkemKKt4SGiEVle5WrFMmCiURIaX9MMttjea+l1xek28jsrFknd7zq+qK9tzBNZ90bDn28AO+PRU9NpAxQZXAhkdIyu9AMyYqdcbgSGnFRxJBFYUABAAAVmFKUOqM7jK5f4UZx5Y7Wm02XxoltSEiEkaOmjc0omjPqJaNO0/czYxcOy3SNkghdSCyZGm1D7xkshql1zHTNaXOUD9SWvbk6BKd6ipfGhkuEESN/2a8Invt+y1a9Z246rzDyF2xn8hF55YsPlFkztBoH3nLZdXKpxc90GiyXGWKAMyA5gSGZXGz31sgLCxs586doaGh1i4EgCuOHTu2dOnSo0ePWrsQAPhXYAQIAAAAOAcBCAAAADgHAQgAAAA4BwEIAAAAOAcBCAAAADgHAQgAAAA4B4+7BICqbObMmfb29jzea/+yd+PGjUePHv0bJQHAmwABCACqsjVr1vTr18/Jyel1d8zPzzcYDP9GSQDwJkAAAoCqzNHRcerUqf7+/q+7Y+mDEP+NkgDgTYA5QAAAAFmjalQAACAASURBVMA5CEAAAADAOQhAAAAAwDkIQAAAAMA5CEAAAGB1lkfa7XOHdmkqCXBzdPEOatxv7sFb+qdrTdrZ4XayLxLML+9oPDXOz675dxmWSiwWqgQEIAAAsK7iK+v6Rr4zZGO6X+cJS9bHLJ/YSnBmYa9WHx/KYYmIiM3VqG45KiLr8l/alc3XV2sxdHArb1zN4DXhNngAALAiy51dwzuOOxu64PTWyQ3cSnNMvz4NhIrWa7/6eUaniYE8MuritKxkllz08t6MW9tZW9pWcslQJSAzAwCA1bD3d0+esFs4aMOPn5SnHyIih4b9BreNdjXkmInInKZSP/aTVlN/2Ufh42Rn7xnSdfHZXJaISH9sZHVxm7XZLBGZlNNDHDuvOr1lbIs67mKHaiHdll7OvXt66Yctwms62VeLGLEj1UxkvrYwUhwx69CeuX2jA1wdXP0i+i4+dR8NNA5CAAIAAGsxX123ZL++07wvO3oyFVbYNJpx+NTBzxoIiahIq0zU3/5xxsrCTot3/753ZtTjI/Nmb82yEJnTNLonvgqZJ0PEPtJpUvVnv5p2rv6Un3/dOzPy8eEZ3aSt52Q2mRazd/OogOubZ666ZKR8TVyS8famSfNvyMau3PHTF20sv83sNXr7fdY6HwBYD1pgAABgJaaru2OvOHSY2dWL+euNjFfjNMViyfSdBz6XiIhIMa7jshOJRSUsUVG8KkkYPjlYQETGK6oEk2enZXvX9vZiiIL7N5j3e2KXb/ctb+XCkNmpe/3vNzJExitx2hKy77TyxA8d3BgialEz61yTtb+e13/Q3baS3jW8GTACBAAA1mG5d/ZMEoU3jHT4u43uq1VZTl0++VhSOgWIzb19u9A5TOLHJ1OSOt4QFCGxJyJLpkb70LvXyLIsZUpJTqGQD0Y2d2GIiAwpyWm8+uFBvNsq1R27NtPntXcri1zCwHq1+IYS/StuMIOqDSNAAACvYLFY8vLyVCqVtQupstzd3X0eZN628EI93F78Xdx0Y+9Xm68FDZraK0hg0MYl8Bt/+K5j2TqjThlPkh4SG2If6DQZTlJ5IJ+IiuPV1/iyjyVCIiJiH2tVqU5R7wQJyo6n1BTW7i5zLtFeTmDeWdTx2YiT+XbGbarR2semEt4yvFEQgAAAXoFhmMzMzI8++sjahVRZ0dHR343ysmPM2VnZFqrzXAZiH+yf8/Hcy/1+nyEgMiUrNYV1e8qdyyKLOVWteez7rtyTIWOCKoEN6xouJCLTdbWupE5fqVPpZsYEpc4S3kVSGmvYPK36plgWUZeSdmkK7aN93Z/ln5SjvyU7N10gFVbaG4c3BAIQAMArtGvXLisry9pVVHUlp5v7frFm6xb1mPmRdmXL9De2fDx5j+i97dOaiYnYJxrlTXtpxNNHABXpVNdspJ8GC8h8U6N95N1YXo1HxObGa1LtpfI6pZuZ01WaR75N5WUzq00JSp0ldLJU9PCY8papsEb6fQv58IjIkr1r1nJdrQ9XtP27JhxUTQhAAABgJbZNJs7usmvYog7N0z4a0C7c+UHCheP7dhx7GDX/wMoe1RkiMiXEac1hUyXlHSrjVaXWGDxcKiYqiVddE0jGhQiJyHRFFW8J/VRa9qSgYp0yUSiZGFI6rGO5rdHc91LIa5p0cTqzh8vVBYOn23/ayv3+uY1ffv2H98RD0995xROGoKpDAAIAAGvhBQz66bjdgmmLt66eurPExt1f0qzrN6cmD25YvSy7ZKjUOTUbKqqXdcjYBxp1pqtMEcAnU5IqXl+3v9SBiCxZWu0DL4WsRulmpmtKnaH+h1L70p1KdKqrfOnIcCble/WTwA/3rrT/dtwnfdfYBkY06bn61LTBT9trwCkMy+LhB2+BsLCwnTt3hoaGWrsQgLdMQEDAqVOn/P39rV0IWF/u1h6+Y2w3Z23vhYYX4DZ4AADgBmNCnMYU1kAhtnYh8GZACwwA/hXZ2dnFxcXWroLMZjzfBYiIyJKlUt3ziojwwS/+QEQIQADwLwkICPD29mYYK8+uuHfvXlFRkXVrgDcCz//jYw9HCe1wvzuUQgACgH+FUChMSEiwt7e3bhkBAQFiMXoeQEQ8Gzt8J8AzGAoEAAAAzkEAAgAAAM5BAAIAAADOQQACAAAAzkEAAgAAAM5BAAIAAADOQQACAAAAzkEAAgAAAM5BAAIAAADOQQACAAAAzkEAAgAAAM5BAAIAAADOQQACAAAAzkEAAgAAAM5BAAIAAOsrSf9j5aQ+TYK93ext7ZxrhrT8cPGvafrXPozx1Dg/u+bfZViIyHjxs3p20UuSzS+8BiBCAAIAAGsrSd42LDKs08Kz/EbDv/h+86aVsweGPTwwo2uz4Xvusq91JDZfX63F0MGtvHlE7AONKtNVERHAr/gagIiIBNYuAAAAuIy9e2BMx2G/1pt/+vTkaLey38r7Dx/Vc1qjpku/+Wl29yl1/7vUYs7P0zu4tZ21pW3p13ptXDwj+0AqfOE1ABFhBAgAAKyIvbt97Iht4vG7d336NP0QEZF9RP+RfVr6iwpYIqKS1MNfvN9c4u9q71A9rPPnB1KNpVsZz02u7dJnw/lN49pL/NusuPrbyOriNmuzWSIy31BqCmpHyF2Ziq+J2MfKmAmdFf5udiJ7dz95l2kH0oyV/b7B+hCAAADAWgyXls07xOszf3pDhxdXCSRjtv62Y7xcQIVxX7ZS9Fz/QDF8yc+x68bWSVzRv9/XCSYistzTau8wcYuGxhhafvr96qHieN0TX4XMkyFin2iUN8SyyCBBxddkTl7Zq82k38Xd527at2/T7PaCM199OO1gfuW/d7AytMAAoCp7/PjxwIEDRSKRtQuBF4WGhq74quf22FTPHqvbOTOlC9lHV46fTSksm/jDCHyiOkjvLxu18Oa76+JiB/sJiKhTY2FCwIC9B5OnhocY41UJBlO94btOfC4REeXHLkkShk8OFhCRKSFOaw77VGb7wmtLxuVE285Ldmz8WCIkonaK3MM/TReJcDHkHvybA0BVtmfPHmuXAK/m4+NjTo49fZsnfyfCtmwZm3tkZvdBB8oDkCB8tvLdJ+vWJ4ZN2jrAr/x6Ja5ew5m5ZjCyZL6l0eW595w6ViIiIjIlqeMNQYMl9kRkyVCpH9SIVtTkVXxNvFoDfjgygMzFD7NSM9NTVNtiLrKSRTIkZO5BAAKAqqx169bWLgH+kvHPezkWUaiLuHwB4zpgf8EAIiL2/sZOgZ9Xj66deOz4/br9OtR5NhG6ODMjRxBY149PBTp1sqjZlOal7TP2kU6T4SSVB/KJqEgbd1UgGxMufOE1+/DS2lmzVu4+m1xgV7NWUKi/PrXEb0iEF+aDcA8CEMDb7ezZs3Z2dtau4hUsFou1S4A3HePq5sKUZKTdtZB/xQRivLY55qRZ/q3CnLwunXwDfJ7ln9zffznNRn/RzJkxXlLHm+oPk5dNHzImqBLYsK7hQiIyXVNq9PUHyx0qvjZoF3Vs8w1v6KrDG7spfOz5lB/bz/c8RYXi7jAOQgACeLu1bt06ODiYz3/jHm9iMBhY9vWe4QJcIwiUS1xMuzetjRu+MNr+6eLim7sm9pt30VhnZpQHL4nPt2TczraQO5+IKP/cwnn7hN1+6uvLY7O1mixnmaLs2T7mDI32kXdjeTUeEZujVmU4yyMC+RVeG89vXq8LGHvxm/dlAiIic9b+HSdLQj5T2L+qOqjiEIAA3m52dnZ//vmni4uLtQt5kYODA8Mw1q4C3mxO7UYPqb/nmyXtG974aHi3aH+7vMzEC/s37EqXNA0R3wuLDhI4eHVv4zRkxagp3jO7+RVqti2ct7W437ave3owpE9QXyFJH4lN6bFK4lXXBJJxIUIiMmjjdCTtI7Wp+NpsI7KxZJ3e86tK4Fty68Le1ctjTj3gN6WixwZyt7HmBwFWgLYnAABYi7jJ/D0xIxT8xD1LJg5+b/D4BT8pbbqsvqDeM2dQ72E9o2yJqTEg5vC33Xi/TO3doef4DVkN5v9xdkNPbx6ROVWje+Inl3uUxmxTkipeX1chdSAic4pKkxcQqXBnKr4WRI3/Zrwie+37LVr1nbjqvMPIXbGfyEXnliw+UWTdjwGsgcEY9VshLCxs586doaGh1i4E3jiurq6pqalv5gjQvXv37O3RXACANxFGgAAAAIBzEIAAAACAcxCAAAAAgHMQgAAAAIBzEIAAAACAcxCAAAAAgHMQgAAAAIBzEIAAAACAcxCAAAAAgHMQgAAAAIBz8MdQX9v+/fuLioqEQmFlnjQ/P99isVTmGQEAAKowBKDX9tlnn3l7e3t6elbmSZ88eVJYWFiZZwQAAKjCEIBem4+Pz6xZs1q0aFGZJw0LC3N0dKzMMwIAAFRhmAMEAADWV5hy5KsRHRuF+bvb2zlVD2rc+5P1F+6brV0VVGEIQAAAYFVs7sWvOgaH9V2T4tV88Izv1q+aPbSR4NLqj95t/NHBe6y1q4OqCi0wAACwomLt1906zH3QY4N61fv1xGULh02e0G1odO8t01aO7vhlxHNXKnN+nt7BScz8f85oLs7XCx3FuP5xHEaAAADAavTqxUPmxEd8tX/ds/RDRMTz6jL1Izlz4/c/UszGc5Nru/TZcH7TuPYS/zYrblrYx8qYCZ0V/m52Int3P3mXaQfSjEREZLz4WT2nbmsvbJ/aq0mot7NjtfBeyy7llQ0iWR6rN0/t31rq7ezo4hUxces3HRwlc3SV/5bhDYEABAAAVsLm7Fnw3dU6Y5aMrPfSeAw/oG6gjeXB3Qeme1rtHSZu0dAYQ8tPv189IihlZa82k34Xd5+7ad++TbPbC8589eG0g/lExOboNBn6kzOH/CToNmfD3u3TI3MOzJj+820LEZt7clqr5mMPGBqO/W7rli87GdcPm/WnjVRR1wrvGt4MGAIEAADrYHMObz9WKJ3+gczmFWv1xcVmnquHmylelWAw1Ru+68TnEhGRJfXnRNvOS3Zs/FgiJKJ2itzDP00XiQREZEhQJZhsZdP27p8cYkNEivGdlp+4WljMUsnFBaO/ze235/L6Th4MEbVxvXmyy8/1IqW2lfqG4U2CAAQAANZhjL+kLnHvElmb/4qVhnhlgsmtdaR/ulKX595z6liJiIiIeLUG/HBkAJmLH2alZqanqLbFXGQli2QiInOaRpfr0XPKyJDSPMU+ysgocA6T+PGfHPl+y+0WC7/o6FE2ecjGq7q7oIZc7o02CHchAAHAv8JsNu/bt08kElm7EHhD1apVK+zJoyfk5Oz0qhRScHLb/tvVu/dtatKtSxY1m9LcoXQ5+/DS2lmzVu4+m1xgV7NWUKi/PrXEb0iEF48oP16VJGw8qWnZlmSMVyWQpJfERn/p1+P5DeZ0qvF07rQhKyNbKIkIwzWQw/CPDwD/ipCQkAMHDjDM/+t2HajCgoODpS2rubP3bqUVUrRzxZUl6mUzf85ttGByS2Hi5/Gm+sPkpanGoF3Usc03vKGrDm/spvCx51N+bD/f8xQVKiQyJanjDUGDpOX5x3xLrcn1bSH3ZO8mXX/sWDfA/ek3Y8HJg8cL63wkc8a3J4chAAH8JYvF0q1bt9DQUGsX8ndKSkrezL8Tp1KprF0CvPHyDrXw+D7266/Od/iykVN5GGELrmwY1nth+rurzo+px7u/RpPlLFME8ImIjJc2r9cFjL34zfsyARGROWv/jpMlIZ8p7InYRzpNhqNUHljeTyvUKZNspFOCBXTPYGQL798vYMmNISLTjc3L99x37B5R91WtN+AKBCCAv2QwGI4ePdqkSRNrF/J3GIbBKAu8rZw6zFrQ8ehHiztEJ478qHu0n+hxasKFX7buvMi0XXxo07DaAtInqK+QpI+kdFYPYyOysWSd3vOrSuBbcuvC3tXLY0494DeloscGckxQJbDhXSXlf6jamKjUGYNHSMXE84mMqKFft2D8D94TZSXqA2u+W38hX9AgSob+LKchAAH8HT6fP3XqVGtX8XcWL16MAARvLUHtYbsv+a1avHr/sW8nr3lArj61Je+O+em7Yd3D3XhEZE7V6J74tZCXTV4WRI3/Zrzq07Xvt1hXI0ga3fGjXbGRw95bvmTxiWE/yjXaR96NZdXKJhSxDzSaLNfSoSN+sxmrxpwdsGpMh1/8whv3GD99SPqw35s1rY4Z0JzGsCyeM/56WrZsaZU/hrpz5843vBdT9ZSUlLi6uhYXF1u7kL/j6uqamprq4uJi7UIA3kxs/vU/j6d5Nmsb7laaoorOfxrZ+vh7F5WzJWiBcRnyLwAAVGFs7h9f9u8xbMnx9Hx90cObf64cOmStYeCCMeFIPxyHAAQAAFUYz2fgwnktH6xqG+Bka+8Z0vuHoj5bf1/Z0R2NY67DHCAAAKjKGKd3Pvvl1qT8+3dy9PZe3h5iDP0AEQIQAABwACN09PJ3tHYV8CZBCwwAAAA4BwEIAAAAOAcBCAAAADgHAQgAAAA4BwEIAAAAOAcBCAAAADgHAQgAAAA4BwEIAAAAOAcBCAAAADgHAQgAAAA4BwEIAAAAOAcBCAAAADgHAQgAAAA4BwEIAAAAOAcBCAAArK8k/Y+Vk/o0CfZ2s7e1c64Z0vLDxb+m6ctWGk+N87Nr/l2GhciSuaqFnd+4k8aXD/E3qwBeggAEAADWVZK8bVhkWKeFZ/mNhn/x/eZNK2cPDHt4YEbXZsP33GWJiM3XV2sxdHArbx6RXhuXwJc3kApePszfrAJ4Cb5PAADAiti7B8Z0HPZrvfmnT0+Odiv7rbz/8FE9pzVquvSbn2Z3n1KX79Z21pa2RERkuq7UFNV/L8KJeelAf7MK4GUYAQIAAKth724fO2KbePzuXZ8+TT9ERGQf0X9kn5b+ogKW9MdGVhe3WZvNErG5GmWKc6iLbnbPSH83J4+6TYd+r3rCEv3tKiKiktTDX7zfXOLvau9QPazz5wdSyxplxouf1XPqtvbC9qm9moR6OztWC++17FIeS1DlIQABAIC1GC4tm3eI12f+9IYOL64SSMZs/W3HeLnAnKbRPfFVyDwZImN8nE6fv3/OvOsRn6zb9fOXrfX7JnSfdDiX/dtVVBj3ZStFz/UPFMOX/By7bmydxBX9+32dYCIiNkenydCfnDnkJ0G3ORv2bp8emXNgxvSfb1sq/7OASoYWGMDbrbi4WKFQMAxG/eEtExUVtePHj7fHpnr2WN3OuewbmH105fjZlMKyARhG4BPVIcIhXpUkDJ8cLCAyp6vU98m175qTP/auziOid/2yzzf4dvufqzt3zfrLVV2Sl41aePPddXGxg/0ERNSpsTAhYMDeg8lTw0NMCaoEk61s2t79k0NsiEgxvtPyE1cLizEEVPUhAAG83W7evGkwGKxdBcBr8/DwMCd/e/o2T/5OhG3ZMjb3yMzugw6UByBB+GxlO2mWOt4QNFhiT0RF2rhrwuaLFvWsXta+EATWrcXX5xfo/2aV/vS69Ylhk7YO8Cu/4omr13BmrhmMLJnTNLpcj55TRobYlBbwKCOjwDlM4sevrI8BrAYBCODt5uPjY+0SAP5Hxgf3ciyiUBdx+QLGdcD+ggFEROz9jZ0CP68eHcR/9LMmw0kqD+QTGa/GaY0RYzp6P529Yb6TdYf1auhv/9erRPFHjt+v269DnWeZpjgzI0cQWNePT0XxqiRh40lNyxtwxnhVAkl6SWz+/TcP1oY5QAAAYB2Mq5sLU5KRdvelGTfGa5tjTprlDRUiY4IqgQ1ThAuJLPfVqgyBj1/1ZyHn1i8Hr7i2ah/J/+tVxuSkdPIN8HmWf3J//+U0G922mTNjSlLHG4IipA5Pd1Jrcn0Vck+0lDkAAQgAAKxDECiXuJgubVobV/j84uKbu8b2m3fRWCc6ysOSodE+8pbLq/GIDLq4eKPhbnZOWV4yZ2ydulRbf9S41uK/WcXw+XxL9u3s8pCVf27hvH3CbqP7+vLYRzpNhmPp6BIRERXqlEk20shgNEe4AP/KAABgJU7tRg+pv+ebJe0b3vhoeLdof7u8zMQL+zfsSpc0DRHfC4sOEpT8qromkIwLERKZbyg1+Ta8iwuHzxKPbeZy9+T6L1ecC5x5fIpMaE74y1XENu/exmnIilFTvGd28yvUbFs4b2txv21f9/RgyJCgSmDDu0qEZeUYE5U6Y/AIqfhvi4YqAgEIAACsRdxk/p6YJwMnbtizZOJeG6fq/kGR7d9ffWFo28LNI2IDomxNSap4fd3+Ugci9olGmVr/k+3Tir6bMbnvStta0sZ9152aMUAqJvbRX64iYmoMiDmc99mUb6f2/kHvXPudrvP/mDu6iRePyJyh0T7ybiyrVtYLYR9oNFmuMkUAZkBzAsOyuNnv9bRs2XLWrFktWrSozJOGhYXt3LkzNDS0Mk8KJSUlrq6uxcXF1i4EAAD+YZgDBAAAAJyDAAQAAACcgwAEAAAAnIMABAAAAJyDAAQAAACcgwAEAAAAnIMABAAAAJyDAAQAAACcgwAEAAAAnIMABAAAAJyDAAQAAACcgwAEAAAAnIMABAAAAJyDAAQAAACcgwAEAAAAnIMABAAA1mS+viTahnkOjycUe9RpOnStpqBsE5N2drid7IsEs1ULLWPJWPmuiGFsGn5944V6LHc2d3XnM4I6n543Wqc2eA0CaxcAAACcVqhVXmODBq36smt1hoiIWGNeyuHlX24ePzro3XNTg/jE5mpUtxwVkXX5Vi6ViIhKtHHxRiImMy3TTM+XVHh68ZdHHlkYB1lkmNB69cF/CQEIAACsyHg1TlPs0qzfsN4dbZ8t7VYt6Wi3g0k3TRTEJ6MuTstKZslF1qvyGdP1OE2he716lJWeWcCSG1O23Jy8dtYWY5269qmuUXJ7q5YI/xW0wAAAwHos99SqTF6YItym4nKDwcDzquHFJyJzmkr92E9aTf1lH4WPk529Z0jXxWdz2bINjVnHv53Qr6XE29XNV9b1893JReVHzjm/elSHyLrVnRzcAqL7Lz37iCUi0h8dUV3cbt3d8v31R0d4idutu8f+9S7PYx9rVDf5ip5dA9mstKynPTD24YEvll5v3r+l2Oguj6jFJ2IfK2MmdFb4u9mJ7N395F2mHUgr74tZHik3ftI9MsDdwcG9VnT/Zecfv3Qa+PchAAEAgPXotXEJFm+ZrPrzlyP23v6tx4pqd+0mERBRkVaZqL/944yVhZ0W7/5978yox0fmzd6aZSEiY3JM7+iOCy67dJq+fvsP4+omrejf4bPjBUSk1yzs1H6Oxn/goi37dn3dmT38+aD554xE5lsqzRP/CIVH2ciNOVWtzfOTy9yZv9ylAmN8nM5SJ7qT3I+fmZZZHoD0quVzD9QY90m92zd40iiJkMzJK3u1mfS7uPvcTfv2bZrdXnDmqw+nHcwnIsvdXz5u0mzMQUvrT9fE7lzWx+741L5TfvuXP2V4BbTAAAC4Zfny5fn5+SKRlRtK9erV69GjhylZqXnCuBhvHT7wkCEiYk2Fd+MPfr/ydPDMvdMa2FBZj0wsmb7zwOcSEREpxnVcdiKxqIQlS/qmCVNOBsw9+cc0uR0RdWwiTjzd49AB1fJWDc+s/V4XPCV+9YR6fCJWavtEeMK7Go+oQKu8LpJNq19+9SuKV18XSqYEC8hw8i92eZ45TanOcZBHSQKKvIo1GQ9ZqsmQJfOn2Wty+/44io5FmmoPkrsxlozLibadl+zY+LFESETtFLmHf5ouEgmIffLbzNEbC7ttOf1Tf38BEbV21sa2/vMyUYfK++iBiBCAAAC4ZuPGjWFhYf7+/tYt4/79+0TsE43yhtlYvHpwj9XPrWOcmy7+6bMmzgwRWe6rVVlOXZZ8LClNbGzu7duFzmESP7756pY1JwRtVnd2upOSQkREZoG7J5/H4xGxJpPZdPXH2Yt9PurRtmGwZ8vJy1sSERmvxGlNoWPl4rIzmRJV8cb6Q6RiItNf7FJBoVZ5jQkfIBX7pvvSobRMM9UU5J9YtPCCfKq6bcH+Rffs5ZH1BcSrNeCHIwPIXPwwKzUzPUW1LeYiK1kkE7E5sWtjHzVZuKSff9nlV9hwUcJ9E+ZMWwMLr6lFixYnTpyo5JOGhoZeuXKlkk8KxcXFtra21q4C4B/WqlWrP/74w9pVlDKcGOMj8BxyuPjpEnPxnVOzGjnwnN/fV8SyLMsW//Khh32XLQ8tZRvoj31Uw671D3cs5pvfNLR5+apm02JVppllTbePLejf0M+BzzA8e9+Gg5aff2hhWXP6d81tfcf+aSg7mCX7hza2XsN/K2HZv9qlYr3nP6ljEzj5rIEtOTjQ1bFvbCFrTFzyjmP9T88VsQV7+7uImn2bZmYtORfXjG4T4mknsHPzC2nYoYPCQ1h3ygUDW3RosLuo8bJb5n/5c4X/AkaAoFI1b97cYrHY2tr+503fABaLxWjE4zwA/jXmdJXqPl8W/dz9XTzbGg07Nqq+6LrIhk9EZEpWagrr9pQ7P5uzo3ns+67ckzH9ce0G02hpypnJga+azlqzzfRtbabrc5Iu/rFnxYx5n40Lantpei2dMoEnHSopu/axuaePKymsZ+kMbP4rdwl57kZ3yz21OtNGFhkmJL5PgLfpj/Tbd/fMXXar3fL9De1MqjhtUY12ipom7aKObb7hDV11eGM3hY89n/Jj+/mep6hQoSUnLb3Axr9WzacFl2Rp4m47ShrUKX9/UGkQgKBSFRYWDhw4MDQ01NqF/FcMBsO5c+esXQVA1VWkiUtka4+N9Hz+6s8+Sbp2h1cvpJ6ASntkN+2lEU+ft1OkU12zkX4aLCDGzs7Okp2eZaBAWyIiy53YUT0WWz45sr6bbnL0xLSPDu4eXUfkUb/5exPzj373a6JAQKZklbaweuvajqXnK9F9v/xIvs9omSejPzbp1btUYNDFxbPBn8odiHi+Ab6UeW33ko1H/Cde7OPFsPc0qlShLCqMLs1drwsYkMpo3QAAIABJREFUe/Gb92UCIiJz1v4dJ0tCPlPYE6O3FzP6O1n3LeTLIyJD4sr3mi6qEXNzV51/+ZOGlyEAQaWytbWNjIxs3LixtQv5r5SUlDAMfi0D+LeYEpXaIocGDYIrXIqMmgsqvahlHR8eEZkS4rTmsKmS8l6X8apSawweLhUT8aM/GBAUs3LMyIB5g2Ti7LPrFyw54z3vaGcvhmdryU0+snzmt9U/inLNT/r1+y93sG1W96vDZy8XFZszD8ds7ekoMehiFy9Yd6XEtq0iWEh/uUuFeq/HafLd3okI5BORk6+f890f56+rNnDvmFABkV4Xp7PUHy93ZPJENpas03t+VQl8S25d2Lt6ecypB/ymVPTYwLi17dVK/NHiEbOdxrfwfHBq9byvr0Z8uaanG37OWAFugwcAAOu4r1Gl86TRioo9cUt+Xr5Fn5KcYSayZKjUOTUVivKb5NkHGnWmq0wRwCci23fmHoqdGKhbOrR7j2ELfxcP3Hb24CdyMZGw6ayfl/T1VC0e1KF1p2FLzjgN2HJq+1B/HgkjR80bGlG0Z1TLxp0n7mZGrp0WaRukkDr8zS7PYZ9olTd40iipkIiI7xvgy5QIm3w+vY0TEZlvKdWPXeURgXxB1Phvxiuy177folXfiavOO4zcFfuJXHRuyeITRcTz/nDdzumKzHWjevxfe3ceF1W9/3H8c2ZhHUFARIFhE3GDgRlA8ooLKFJqaJiaZt4yTcol11wS90SuJfZTI5fczX1NDS3NzC2H2UBRUdkGVBRFZJ31/P4QNU1NSx3svJ9/1cyccz4Mj4fnxfkehu79Evfw31pyePeYhz8CCV4OhmXx+UvPJjo6OjExMSoq6mUeNDAwcNOmTa/KytETREZGJicnv0JXgJycnKqrqy09CMDz1Llz50mTJnXq1MnSgwBYEq4AAQAAAOcggAAAAIBzEEAAAADAOQggAAAA4BwEEAAAAHAOAggAAAA4Bx+ECADw9+n1+sOHD7u4uFh6kGdQXl5uNpstPQWAhSGAAAD+vo0bN44cOdLf/1X6SwYXL168evWqpacAsDAEEADA3+fm5vbaa6+lpaVZepBn0LlzZ3d3d0tPAWBhuAcIAAAAOAcBBAAAAJyDAAIAAADOQQABAAAA5yCAAAAAgHMQQAAAUFdUbOtX36rV5wrj89iZuWBhR2uGsWoz74LpoWcur4pz4TMC/3HHDc/jSPAqQgABAEAdYcw6pawUSVu3eC4f0VKjlmcYiEzaPO2DAVR5ZO7sfTfNjG1IWKDweRwJXkUIIAAAqBvMxYr0fJ4kQmrzPPZmPC9XVboEBDiX5msr2PuPm7KXJK42+De1F7YMl9o/jyM9Bmtm//pFYDEIIAAAqBv06lMZJnFYaGOe6eycMLvQxB+2Te8T4eMkcvIK7TP312t3/n6H4eRnAQ49lpzYMKFXZCsPx3oNg3rN//32n1qDLVUpLvJl8XF+bGFe4b1LQOyNXTO/Ot+hX7SdwUUa6ssnYkvTl3/aXebtbGtt7+IlfXPSrry762Lmm+krxvYM83ERiVx8I/rNP17K1u77sZuYio8uGhbXpllDe6GVc8jwrasHu3sm/Pxi3zf4WxBAAABQJxjPn1KU2cpaBwqpXCU/ZyhaOXrWhZDhCzeunRljTpvS6+MN11gitkSjKtAdnvLBWkGPad9t3zA5rGTX55PXFT38180MGXKN2T+im9SL/4c1MJ0iZfquxiPGBhRd4AWHS4Rkyl7YK2b0T3Y9p6/csWPl1NcFv/3v/Um7y4nIfHXPJ5Hth+02dx6XumXT/N62Byf0GZ9WTk/YhL2+b1j7mEm/2PeYuWbr+uSevLWDR20pC44IfrlvJDwV/CkMAACoC9hSZfpFptVAmT0ZTsjVNWTfbeGhb99wZogoyr3wWOSSH4/r3u3JZCoyjTYhk7bvHNPSiohkI7ulHDpTWf3QJSBTXrqyRCQNl/hUuVWrCm6w5M6QWbt2auqtPmsS6ECYsclAqTNjLjiVZdM9eeOKTyRCIoqV3dq7drK1tYDYsrQpH6+o7LH6yNp+3gIi6uyo3tL5l1O5xth6j91k38Shq5gPfzi2MMaJIXo92r3wt44LReGhTi/9zYS/hgACAIC6wKD5XWVw6xXmzTcXKRSXbWO+mfG6M3PnOaFfgC9fX6MzkalQpbnVIH78Ry2tiIiIvVlQUOEYKPHiP7i3SnX6WSZoQLCdOF9MP+RpTeQuKD+UNOeEdIKyS8XOpGJ7aVhzAfF8B3y7bwCZqm8U5mrzLym+X36SlSSFWLMlW5ZsuRk5J7mvd+15UtgmKfOaUSgS8HiP3sRcuCplU3XPlbM6O92durGHm6B+s9YBONXWRVgCAwDglkuXLnXu3JmxtG7d3rh9/dqNins3z+SkK0uspK0lAqpWn8pkXovr6sbcndlUVFBEjb08ragqQ3FO2Da2naj2GUOGIpMkYRKrB79Iw5lTap1HqKyxoKG32OZKnlZPxrOpieusB88c5KtXy89QUHiIDbE3fv/2ky6tGtZzEAfHvDt+4baTuTVeYaFuvJrfDx3TSd/s6nn/LMm3ETmIrHmP24QpPZx2kjr0iHW6P3VhfhEvOEJq/cK+l/APIIAAALglNzeXrQP2rIjv7SV+67vam3fYGyeOZlHz1qGOjPGcXFVp7yl2uV8Sl/anZTu2iwoWGs8pM/TNQoPv9o8pR6m6JZZJXZkHvkZzsVKptQoJCxQS39PHw6jNL7q6bfr8nNip49rYGs/K1VWNZTJ3ozqpa8xkVcvP92bfKr+Rf+b4pg/8DXbS8FZCc0lefoWVt6/7vZNkTaHqt98vlukeu4mp6FKe0dVLbHtvCL1i7/5i7/DQh2aDOgIBBAAAFmDMzc4xWnt6NeIREZnzt31/RN/09dcD+OwNZXqOsbIgv/a3vsh8ZXNiisb3/aFdROxNjaqgXrDU7+6CV6Um/ZxVcNjDHxyk18gz2BbhUhERX+wjJu3Zrckz93mPmt7bjWFvqBS5wpDwQPp91TKNz/DUL/uHe9rziUyFOzcermnZWmZPjK29HaO7XHh3BH3Wwnfa9fgyvebxm/DsRPbsjZzcstqbkfTnUj9fetFW1rolFsDqJnxfAADAAgTeLZrZVaUlj0s1drDO2vLll0c9Bm8fJROQXiPXmBrUP/PFfyfbj+vkcu3YitnzfvYY9cPk16zJkKnIZIPiJHc/vtCQla4xtBgSbPfgvo3n5apy59dC/fhE5CD2cry6ZtbShu9tH9ZKQKTTyDXm5iOl9Zjb1lbmwiPbflQIxDU5J7YvTln+63V+O6oq1TPOXXp1shs6d8hUh5FRrtd/XTxj3pnQ2anxLlWqx23C843rEzHjsynvT2MT2tYrPLA4ecWpGqG0tcz24a8c6gZcAQIAAAtgGr/39bKPA6+uHTXwo2lbSv4zc+9PC95wZch0Sa4s83t/5ZqPhDvG9nk7Yf4Rq/jFvx5M6uDIkKlApb7pIQ1pWHvuYq+rVIVOITKfB++AZsvU6Rd4weHBQqI7l4CYGmHkxMkxDnTnXqNSJ2moH18QPvLLkbIrS/pHdeozatFx0Uebt4yVWh9Lnnuoinge7y/dNFmmXZrwVvd+iXv4by05vHtMkNWTNuEHDF+3YaxE+92Id/qPW3stZu64NgKPsDB3nGfrKIZl8UmVzyY6OjoxMTEqKuplHjQwMHDTpk2tWrV6mQd9ESIjI5OTk9u2bWvpQZ5KTU2Nk5NTdXW1pQeBumv//v0pKSlpaWmWHuTf49b6t8TDbFYVbugl+usX12G6Ax/59L65QLu1r4OlR4FHQpkCAEDdYciUq4yBrWV2f/3SOs108VT67RYRshf5tzbgH8E9QABQ5/To0ePUqVN2dq/AObC6utrG5rn85SogIiJzoUJR7BYa6vmK/3jOlqkybrbsEvnQ4hzUIQggAKhzXF1dhw8f3q9fP0sP8teOHDmyfv16S0/xL8Lz/uTAjQSh7av+R9oZ53c35w2w9BTwJAggAKhzBAJBgwYN/Pz8LD3IX7tw4QKfjx/ynyOele0rcOXvKeDDf+q6V/wiIwAAAMCzQwABAAAA5yCAAAAAgHMQQAAAAMA5CCAAAADgHAQQAAAAcA4CCAAAADgHAQQAAACcgwACAAAAzkEAAQAAAOcggAAAAIBzEEAAAADAOQggAAAA4BwEEAAAWJz5pnrD9EFvtpP4ONer79Gsbd/pu3N0f2M32kVRtl4jDhue4rVG+aQWQubPBF4jfn14e3PBwo7WDGPVZt4F00PPXF4V58JnBP7jjj/NQaHuEFh6AAAA4Ljq00vfixu5x9j2w2GfDhglqsr5dVXKnF6dtNvly99swDzLnnRqeSZfOij4Kc5tbIlKkcsED10+Jab+A8fguYaECx96cY1anmEgYrR5WhM15d9/ovLI3Nn7bpoZUUhY4MMbQd2GAAIAAAsyX948uOuIo62+OLJ+TGvnO8sSfXu3Fso6L/nfus+7jfJ7YKXCVH5bJ3Kwe0wVGc+nq6qavxPq8BTVpNfIM8xe7w4c+PZ//rJcjOflqkqXgAAqzNdWsOR8d/em7CWJqw3+Te1zncKl9n99TKhLsAQGAAAWw17bOubTrcKB360Ze7d+iIhEbfr+t0uEk77ERESGY2Oa1O/93fGVI16XeMcsuGhmS9OXf9pd5u1sa23v4iV9c9KuPAMREXtLlX7JsVV9zdT4MG9nhwZN2w36RlHGPvLAphyFqtRWFhH0FNdt2FKV4iJfFh/nxxbmFd5bA2Nv7Jr51fkO/aLtDC7SUF8+0eMGIyLzzfQVY3uG+biIRC6+Ef3mHy999Fzw0iCAAADAUkxnlibv1HWbMbur64MXbaz+8/neX3d/1lpIZC5Wqy8z8qRBy/XR475ZPKTZpYW9Ykb/ZNdz+sodO1ZOfV3w2//en7S7nIgMGXKNrnzntBnnQ8cu3bxudmfdjk97jt5761GpUa5KP0eBr4U+zXUbQ4ZcY/aP6Cb14mvztHcDSKdImb6r8YixAUUXeMHhEiGZsh83mPnqnk8i2w/bbe48LnXLpvm9bQ9O6DM+7Z+9dfBPYQkMAODvKy0tzc7OnjhxoqUHefU0adJkyAcRW7ecFr0xJc7tCWtW+gxFpt4YMHjzoYkSayJz7rosm+7JG1d8IhESUazs1t61k62tBUSmfIXyGjn1ST285u1GPCLq6HXleOuvN/yyuPtbtg/t1HA6XV2jzx3rzx/7x4f5XiMOXvy/Dg9eFTLlpStLRNJwiU+VW7Wq4AZL7gyZtWunpt7qsyaBDoQZmwyUOjPmglOPHowtS5vy8YrKHquPrO3nLSCizo7qLZ1/OUX0xvN4I+FvQgC9GgwGQ1ZWVk1NjaUH+acqKirMZrOlpwB4biIiIjp16uTk5GTpQV499evXNxcf/e0cBQ0JEz3hdaYclea2S/yE4RJrIiLi+Q74dt8AMlXfKMzV5l9SfL/8JCtJCrEmqlTLzwo7JCXFN6pd3BD4NfXl68ordEQPBZD5ikpZJHht9OpxbW3++DjPNaT1n9bEKtXpZ5mgAcF24nwx/ZCnNZG7oPxQ0pwT0gnKLhU7k4rtpWHNBY8djC3ZsmTLzcg5yX29a0+5wjZJmdeMuGfawhBArwZbW9svvvhCIHjlv1+5ubklJSWWngLgufH19V22bJmlp3hVGeWTisy8Vg2cH74bw3hh+/9WnW02cEKvZoIKjTLbuv34DrWRxN74fUli4sKtR7MrbN19m7Xy1uXWeH0Q6sYjwxm52hA6rKvHvb2ZLhdeZt3aeNtXbu/v8faGMpaIBEFT05UzAtTyTDZg+MC+vUL+8p9Vw5lTap1HnKyxoKG32OZKnlZPoRdSE9dZD04b5KtPk5+hoD4hNo8frGb/oWM66eddPe9/lXwbkcNzeQfhH3jlT6gcoVarLT3C8xEZGdmwYUNLTwEAdQJjY2vLmK4UXjGT/x8aiL2+c9on00/1/elzAZEhS5lhbP6h9E7/6NVJXWO+5A1atHdFD5mnPZ/Kt/QVH6fwVkIyFykVBQJPr0b3+ydnz+7TTp1mhAmt9cmnzk5jiYh4osZ+AmNmurpcFCwLeIpzoLlYqdRahYQFConv6eNh/Dm/6Oq26fNzYlN2trE1KuTqqsaxMnfjYwczl+TlV1h5+7rfm6umUCUvqidp7e/4TL/iD88ZAgjgsfh8vsFgYBj8I/Wy8Xi84cOHW3oKeOH4TTt2EM9MXb9aOWxW2N1FKt2F1Z+M2Wb9zoZJ7e2I2OtqVaFjiMyHT0Rk+H3VMo3P8JNf9r9z4cZUuHPj4ZqWn8ns7/xau0EfdKXETJ48IjIVrJ/wlbp5wjed7UhgJw6of/+4bIlKcZEJHBRs8/BEj6DXyDPYFuOkIiKe2EdM2rNbk1fs8x51srcbwxarFLnCkPBA+n364wZjdPZ2jO5y4TUziXlEpM9a+E67pMbLL272f57vJTwzBBDAYwmFQqPRaOkpuCghIaFly5aWngJePJvIUVPf3Pxh0hsd8oYOiA1yvJ554uCOjQduhM/atfCtRgwR6TOVp0nSW2JFRESMlbWVufDIth8VAnFNzonti1OW/3qd346qSvWmy+mqciveyTmDE+2Gt69/9fCy2QuO+U05OD7kz/faGDLkGr1dy1uKH3ZpHniC30gWGyEWku7UouGL1JJhi0dE8M/LVeXOr4X68YnIQezleHXNrKUN39s+rJWASKeRa8zNR0rrMbcfOxjj3KVXJ7uhc4dMdRgZ5Xr918Uz5p0JnZ0a74yfrCyNhWcUFRV16NAhS0/xqmrbtu3Ro0ctPQXUdUOHDv32228tPQW8FObyrE0Te0jF9W2ENg6NmkX2Gb/s+BX93WeNZ5PCbQI++91Q+/+mwt1jOwW4iuo1bBoWM2DqVtX+yTInW9f+m2/eWP2mQ/DnOzeOjm7iZFuvccvIt8evUd8yP+qQxuz/vfbIO5D5XiN/1bMsa1BPkwiE0lmnjay5ZFU3W5vYZcXmu+MIGYcuqXkmlmVZY9YXoVZug9NqnjBYKcuypqL9U99s6WprZeMolsaNWf2YueDlYlgWn8X0bKKjoxMTE6Oioiw9yCspMjIyOTm5bdu2lh4E6rSEhASpVDp06FBLDwIA/1r4IEQAAADgHAQQAAAAcA4CCAAAADgHAQQAAACcgwACAAAAzkEAAQAAAOcggAAAAIBzEEAAAADAOQggAAAA4BwEEAAAAHAOAggAAAA4BwEEAAAAnIMAAgAAAM5BAAEAgAWxN9fE2fOYu3g8ga1Lk3ZDlmsqnnVPZu2iKFuvEYcNj3quYGFHa4axajPvgumhZy6vinPhMwL/cccftSH8awksPQAAAHCZMVOu1rt2m7XwgxZ8IjLV3Lh4cPlXKz7u1zAk44uwZzlJ6dTyTL50UPCjtqlRyzMMRIw2T2uipvz7T1QemTt7300zIwoJCxT+wy8FXikIIAAAsBxzgUJZLAzrldC7VwOm9rE+QaW/tVt07qKOnjqATOW3Ky+mq6qavxPqwPz5aeN5uarSJSCACvO1FSw5332JKXtJ4mqDf1P7XKdwqf1z+HIehzWzDO8Rg4HlYAkMAAAsp0otP8M2iwhzul8HbFnOpeuCoLBgmzv/W5q+/NPuMm9nW2t7Fy/pm5N25d1ZqzIcG9Okfu/vjq8c8brEOyYlXZF+ybFVfc3U+DBvZ4cGTdsN+kZRxtbuQqW4yJfFx/mxhXmF99bA2Bu7Zn51vkO/aDuDizTUl/+EYxGZb6avGNszzMdFJHLxjeg3/3gp++TxiEzFRxcNi2vTrKG90Mo5ZPjW1YPdPRN+fsFvKDwtBBAAAFiM8Wy6qsoptHXtqpRZV3rx0IKPphxuOvarYc35RGTKXtgrZvRPdj2nr9yxY+XU1wW//e/9SbvLichcrFZfZuRJg5bro8d9s/h9o1KjK985bcb50LFLN6+b3Vm349Oeo/feYonIkCHXmP0jukm9+No87d0A0ilSpu9qPGJsQNEFXnC4RPikY13d80lk+2G7zZ3HpW7ZNL+37cEJfcanlT9pPPb6vmHtYyb9Yt9j5pqt65N78tYOHrWlLDgi2CLvM/wZlsAAAMBS2BKlIs947fwbtt/de4wR+g3c+Nusdg4MEZkLTmXZdE/euOITiZCIYmW39q6dbG0tICJ9hiJTbwwYvPnQRIk1mS7MU14jpz6ph9e83YhHRB29rhxv/fWGXxZ3f8sqL11ZIpKGS3yq3KpVBTdYcmfIrF07NfVWnzUJdCDM2GSg1Jl57LHYsrQpH6+o7LH6yNp+3gIi6uyo3tL5l1O5xth6j91k38Shq5gPfzi2MMaJIXo92r3wt44LReGhTpZ4n+EREEAAAGAperVcY/Z5Z37y2153FiQMt3MOpiatHPSurNXPI5vxiec74Nt9A8hUfaMwV5t/SfH98pOsJCnEmsiUo9LcdomfMFxiTURUpZafFXZISopvVLuyIfBr6svXlVfoiAzq9LNM0IBgO3G+mH7I05rIXVB+KGnOCekEZZeKnUnF9tKw5oLHHost2bJky83IOcl9vWvPmcI2SZnXjEKRgMd79CbmwlUpm6p7rpzV+e7SnrCxh5ugfrPWATjt1hX4TgBAnWMymUpKSnJyciw9CLxAzs7O9esVKVSltq3fHtK7l+jeE28HlR5rMz/tWOmIZg3oxu9LEhMXbj2aXWHr7tuslbcut8brg1A3HlGZRplt3X58hzsbGs7I1YbQYV097t3YYbpceJl1a+NtT4b0U2qdR5yssaCht9jmSp5WT6EXUhPXWQ9OG+SrT5OfoaA+ITbEPu5YNfsPHdNJP+/qef+mEb6NyIHosZswpRvTTlKHZbH372wyFeYX8YIHSq1fxnsLTwMBBAB1TnFx8Z49e1asWGHpQeAFCg8P3/jtm+nnmZbvSGwfeMZkMpFN/fp2jF49p2vMl7xBi/au6CHztOdT+Za+4uMU3kpIZMhSZhibfyi90z/ma0pFgcDTq9H9/snZs/u0U6cZYUJzsVKptQoJCxQS39PHw/hzftHVbdPn58Sm7Gxja1TI1VWNY2XuRnXSY45lLsnLr7Dy9nW/t++aQpW8qJ4kpGLxYzYx5V7KM7pGi+9/XXrF3v3F3vGhrvhNsDoDAQQAdc7u3bstPQK8DIbfRqn1LrFS7z98Lg9bdnjN9px60VMjbQ2/r1qm8Rl+8sv+IQIiIlPhzo2Ha1p+JrMnYq+rVYWOITKfO5vqNfIMgz7oSomZPHlEZCpYP+ErdfOEbzrbkf4XeQbbYpxURMQT+4hJe3Zr8op93qNO9nZj2GKVIlcYEh5Iv09/3LEYnb0do7tceM1MYh4R6bMWvtMuqfHysyNOPm4Tnp3Inr2Rk1vGRjZgiEh/LvXzpRdtu7VuiZNu3YHvBQAAWEaRQnmZdTbl7du1iyEiYg23Lvy4cM7qW9Fff9GnIcPkWluZC49s+1EhENfknNi+OGX5r9f57aiqVE+iTOVpkvSWWBERkelCuqrcindyzuBEu+Ht6189vGz2gmN+Uw6ODxGSMUuuKnd+LdSPT0QOYi/Hq2tmLW343vZhrQREOo1cY24+UlqPuf3YYzHOXXp1shs6d8hUh5FRrtd/XTxj3pnQ2anxLlWqx23C843rEzHjsynvT2MT2tYrPLA4ecWpGqG0tcz2SW8HvGQsPKOoqKhDhw5ZeopXVdu2bY8ePWrpKQCgTtjxrtMfV4QYvnV9r9C3Jm7KqrjzvKlw99hOAa6ieg2bhsUMmLpVtX+yzMnWtf/mUuPZpHCbgM9+N7Asy7LmG6vfdAj+fOfG0dFNnGzrNW4Z+fb4NepbZpZlWXPJqm62NrHLis0sy7LGs0nhQsahS2qeiWVZ1pj1RaiV2+C0micdi2VZU9H+qW+2dLW1snEUS+PGrL6z7yduYsj/YWJ3ibuDvbPffwbM3Tg7xt5v9FH9y3+P4bEYlmUtFl+vpujo6MTExKioKEsP8kqKjIxMTk5u27atpQcBAHhpdAc+8ul9c4F2a18HS48C9+CDEAEAAF4k08VT6bdbRMhe5N/agGeGAAIAAHiB2DJVxs2WXSJ9+H/9Wnh5cBM0AADAC8Q4v7s5b4Clp4CH4QoQAADAC4UP/6mLEEAAAADAOQggAAAA4BwEEAAAAHAOAggAAAA4BwEEAAAAnIMAAgAAAM5BAAEAAADnIIAAAACAcxBAAAAAwDkIIAAAAOAcBBAAAABwDgIIAAAAOAcBBAAAFlaT/1PKJ93DmzSsZ2Pj4OYn6zpiuaKM/Ue7NPw6wsu2w/8VmJ/8MnPBwo7WDGPVZt4F00PPXF4V58JnBP7jjhv+3ghs6doeIuf3dlX9vc3hBUMAAQCAJdVkLHgzvOvMo/axY1NWb9zw7fQB/gWrEmLfW134F/HyJGy5rmHUoP928viLs1yNWp5hIDJp87QPBlDlkbmz9900M7YhYYHCvzeDMTNdbZK0ltr8vc3hBRNYegAAAOAwc86yTxOVsq9P7f6kqdWdh97qHcHKIr7a/kvZ++85PfBi1swyPOZpdss4d0lc3eVPD5vKb+tEDnb3dmE8L1dVugQEUGG+toIl57tPmLKXJK42+De1z3UKl9r/za+sUKG47vFaWGNcaaib8H0BAACLMV9J23nCIO35lr/V/QcF/rFDh3/cM/BOeZiKjy4aFtemWUN7oZVzyPCtqwe7eyb8rCfS7R/SyC526dW7a2W6/UPc7GKXFrNEugMfNbKLWXKFJSLDsTFN6vf+7vjKEa9LvGMWXPzDdSW2VKW4yJfFx/mxhXmF9y4BsTd2zfzqfId+0XYGF2moL5+IanL3zuzfQeLtZC9qFNh94q7c2mUxw8nPAhx6LDmxYUKvyFYejvUaBvWa//ttloioWi0/I5S1biUkIn3ulo+C6ou7zZf/w5U9eH4QQAAAYDGMtUM9K8OJheMW7M24prv7oEPk8JSvBkmtiNjr+4a1j5n0i31ECW3QAAAP4klEQVSPmWu2rk/uyVs7eNSWsuCIYCGZchSqMu9QWYPayzamXKX6tpc0xIUhU55KUyaWhbgyROZitfoyI08atFwfPe6bxUNa8u8f3ZAh15j9I7pJvfh/WAPTKVKm72o8YmxA0QVecLhESJXy2Z1k8cuuywYnr9uydLh/1oJ+fedlGomILdGoCnSHp3ywVtBj2nfbN0wOK9n1+eR1RWYi41m5WteqtUzElsnnv9V+yMk2y47sGBPu+FQXsOAlwBIYAABYDNOg99z/+yln9Pqx3TeMF3lKO8bGvt41/u3uMjchEbFl+yYOXcV8+MOxhTFODNHr0e6Fv3VcKAoPdWKoQp1+3jpkUvO757GqDOV5oWR8CwFReYbinDBoTAsBEekzFJl6Y8DgzYcmSqwfPLgpL11ZIpKGS3yq3KpVBTdYcmfIrF07NfVWnzUJdCDM2GSg1NmkSUqYc7HjUvmW/3oJiKhbW2Gmz4Dtu7MnBLU0ZioyjTYhk7bvHNPSiohkI7ulHDpTWc0Se0OlyG8QGsLb+2n0wM31x+09PLmtM+KnLsEVIAAAsCDr5u+v1hQVKPatmD04UnRhW/KI+PDArl+f1hGZC79P2VTdc9aszk616SBs7OEmqC9rHSAgw2m52tgqXGpXux9jliLD0FwWbEdkPKfM0DcLldgTkSlHpbntEj9h+MP1Q0SV6vSzTFBYsJ3YR0yFdy4BlR9KmnNCOmFKlwqlotheGtbcdGTpsqzA0UkDvO6Wll2jxo6MXm9gyZSn0txqED/+o5Z3FvDYmwUFFY6BEi8+6TVyjdEmY3bU24vUdnGfjUb91Dm4AgQv1eXLl6dOneri4mLpQQDAwkJCQiZPnnznvxk7d+kb70vfeH9iSk3uztFx7yydPf/g0O8iDqedpA7LYp3utYOpML+IFzxQak1mrVJ5xVUmE9f+HM+WqFUFjsFSHz6x1zSqAodgqR+fiCo0ymzr9uM7iP48geHMKbXOI07WWNDQW2xzJU+rp9ALqYnrrAenDfLVp8nPUFCfEEFG6sFrTfu+4X9/5axaW1Ai8GvqxaeqDMU5YdvR7e7u3JChyCRJL4kVmc6lK28aci/Yzl46fvPQlesOfBnb0+EFvZPw9yCA4KWaNWsWwzBC4d/8rVIA+Ndwd3dPn9yi7dpOaTmLou7/k2Dj2+3dGPfvvhcKeaaiS3lG12ix7b0n9Yq9+4u940NdGdJp0jN5wYMktWcx9taRg+kUGB9kRWTIVGSygXFBQiIyZCkzjM0/lD6if8zFSqXWKiQsUEh8Tx8P48/5RVe3TZ+fE5uys42tUSFXVzWOlbnXZJ/LJ7GP5/3+ufXTniNsxMz2joxRoczQNxsYfHfnphyl6pY4SurKULkq/bx9TMrhncP9tYKl/zdr9e7rPQa44iJQXYIAgpfq3XfftfQIAFBXnD5kw948f77YHOV5734Mk3bb4i2Xm7z333ZWvEKRPXsjJ7eMjWzAEJH+XOrnSy/admvdUkDGMwp1ZaPOTerdaYoazTcp+8o9Pw5xZchUoFLf9GgrbcgjYq+rVYWOITIf/p+PrtfIM9gW46QiIp7YR0zas1uTV+zzHnWytxvDFqsUucKQ8EAhU8jnmwuKrpjJhU9EVH5szowdwh5r+4h57HWNqqBe7ZUmIqJKTfo5q+DxLe6sz5lCxvTwtyaeX+/+bRI/W72tqH+CJ247qUPwzQAAAMto/va7rXmHJnZ/b+bSTbv27NmxfvG0D9pFfHC4+ey10/5jQzzfuD4R7I9T3p+2Zs/+Hd+O7frG9FM1wqDWMlsitrqq2qTdu3z9sQzlL2sn9Xhn/ukaG4mshZCoJkNxViCRtRQSkT5TeZokoRKrPx/ceF6uKneWhvrxiRgHsZfj1e9nLa3sP3NYKwGRXiPXmJuHS+uRqEPPGIeMBQnjV6b9enDbgg87dv+/6r6L58U3YO5caQoKldy9fGXIStcYWoQF25H5slJ5tVFISCMeEfHE8f07ML+t2XjJ9OcpwHIQQAAAYBmCFmN3Hfi/fo3OrJgyuE+vvoMmfHO4OnLGz8ofx4WJiIj4AcPXbRgr0X434p3+49Zei5k7ro3AIyzMnUckDEuYMSi0altCdNvuo7YyHy2ZFGbTTBYsIjKeU2TomsqCRURkylVpyryk0gZ/Xntiy9TpF3jB4cFCIiK+2EfM1AgjJ06OcSAiU066stRJGurHJ6bxgOV7v+7B2zPh7TfiR35X2HrWz0e/i/fgUe2VJmlIw7t3IV1XqQqdQmQ+fKpRyzN5QbJWdxZZmEY9+ne2Tl+7/rTx5byv8FQYlsWHMj2b6OjoxMTEqKgoSw8CAMApugMf+fS+uUC7tS9uJ4Z/DleAAADgVWC6eCr9dosI2d/8yxQAD0IAAQDAK4AtU2XcbNkl8lH3MwM8O/wWGAAAvAIY53c35w2w9BTw74ErQAAA8ErAp+jA84QAAgAAAM5BAAEAAADnIIAAAACAcxBAAAAAwDkIIAAAAOAcBBAAAABwDgIIAAAAOAcBBAAAAJyDAAIAAADOQQABAAAA5yCAAAAAgHMQQAAAAMA5CCAAAADgHAQQAABYkLlgYUdrhrFqM++C6aFnLq+Kc+EzAv9xxw2WmQ3+zRBAAABgQTVqeYaByKTN0z4YQJVH5s7ed9PM2IaEBQotNBz8iyGAAADAcozn5apKl4AA59J8bQV7/3FT9pLE1Qb/pvbCluFSe8vNB/9aCCAAALAYtlSluMiXxcf5sYV5hfcuAbE3ds386nyHftF2BhdpqC+fiGpy987s30Hi7WQvahTYfeKu3NplMd3+IY3sYpdevRtPuv1D3OxilxazRGQuOb444Y2wpo0cRM4+Ef2+Onrz7qsMhQe//rRvtMTDyVkcEjdxa3bVy/yqoS5AAAEAgMUYMuQas39EN6kX/w9rYDpFyvRdjUeMDSi6wAsOlwipUj67kyx+2XXZ4OR1W5YO989a0K/vvEwjEZlyFKoy71BZA+bOpqZcpfq2lzTEhSGdak6316epvN9LWr1j87zu7N6JA2cdMxCRIXv52xFdvzhVv9vkZRu+HdH03IJ+b3x2sMJCbwFYiMDSA7x6dDpddna2g4ODpQcBAHiFubi4+PiI89KVJSJpuMSnyq1aVXCDJXeGzNq1U1Nv9VmTQAfCjE0GSp1NmqSEORc7LpVv+a+XgIi6tRVm+gzYvjt7QlDLCnX6eeuQSc3vns2qMpTnhZLxLQSkP7zkG02L8RmLPw3gE7HBNmXCQx4NeWTOX/np+MM+0w//PElqS0RdI+2yjrz1wy5FSqcOuNeIQxBAz4zP5y9atMja2trSgwAAvMIiIiIWL05Sp59lggYE24nzxfRDntZE7oLyQ0lzTkgnKLtU7EwqtpeGNTcdGb8sK3D0+gFed09Zdo0aOzJn9QaWDKflamOr4VK72meMWYoMQ/MPgu2IjEajyXhmzdS5nkPf6tKmhWv0mJRoIjJlrk49JIhZ3N3h8qVLRERkEri48nk8rIhwDALomR05csTSIwAA/CsYTpxS6zziZI0FDb3FNlfytHoKvZCauM56cNogX32a/AwF9QkRZKQevNa07xv+/HvbVWsLSgR+Tb345itK5RVXmUxcWy9siVpV4Bgs9eET8WNmrptZNW3J3P9uTmTtPCN6jfkq5dM29fMOpGXpS9RDJNv+OIlV1Me+fAIuQfECAIBlmIuVSq1VSFigkPiePh5GbX7R1W3T5+fETh3XxtZ4Vq6uaiyTuddkn8snsY/n/T659dOeI2xEl/aOjE6TnskLDpfU/jDP3jpyMJ0CQ4OsiIj47jGTvz+eX3I165f1k0KLN3w24tuzJuOFsxeY/3x1ycQ+QHdomCdOiNyC7zcAAFiGXiPPYFuES0VEfLGPmLRntybP3Oc9anpvN4a9oVLkCkPCA4UMn883Xym6Yq7dqvzYnBk7hD0+7iPmGbMV6spGAU3q3bkDukbzTcq+ck9piCujOzA6pOVbqRdNRNYNmnd4Z9SH7Z14fIGAGFtbW/OV/EJ97d7Ml7d8FBE6eHMx+8gR4d8LS2AAAGAZ5+WqcufXQv34ROQg9nK8umbW0obvbR/WSkCk08g15uYjpfVI1KFnjMMHCxLGe0zp4VWp+n7OjPXVfb+fF9+AIUN1VbVJu3f5+vh6Er1my9wvlp6usekiayEkno35Vva+lClfNxoa7lR+7sdvZm9kYxb39ecLvd4d0Gz5wmEf+cwYGGJ35eiyL5J/85ixv7sbY+l3A142FgAAwBJWdbO1iV1WbGZZljWeTQoXMg5dUvNMLMuyxqwvQq3cBqfVsCzLmktOLhrUrqmrva2De2CXhIW/XTXW7sFw6fuPXhPXs7YSNZbETdp6eF5H+9DZZ4wsy5qv//blgP/4u4qsrewaNGnTe/KmrPLajXS5P0yJk3g42ju6N283YPau8xUv/SuHOoBhWVz2AwAAAG7BPUAAAADAOQggAAAA4BwEEAAAAHAOAggAAAA4BwEEAAAAnIMAAgAAAM5BAAEAAADnIIAAAACAcxBAAAAAwDkIIAAAAOAcBBAAAABwDgIIAAAAOAcBBAAAAJyDAAIAAADOQQABAAAA5yCAAAAAgHMQQAAAAMA5CCAAAADgHAQQAAAAcA4CCAAAADgHAQQAAACcgwACAAAAzkEAAQAAAOcggAAAAIBzEEAAAADAOQggAAAA4BwEEAAAAHAOAggAAAA4BwEEAAAAnIMAAgAAAM5BAAEAAADnIIAAAACAcxBAAAAAwDkIIAAAAOAcBBAAAABwDgIIAAAAOAcBBAAAAJyDAAIAAADOQQABAAAA5yCAAAAAgHMQQAAAAMA5CCAAAADgHAQQAAAAcA4CCAAAADgHAQQAAACcgwACAAAAzkEAAQAAAOcggAAAAIBzEEAAAADAOQggAAAA4BwEEAAAAHAOAggAAAA4BwEEAAAAnIMAAgAAAM5BAAEAAADnIIAAAACAcxBAAAAAwDkIIAAAAOAcBBAAAABwDgIIAAAAOAcBBAAAAJyDAAIAAADOQQABAAAA5yCAAAAAgHMQQAAAAMA5CCAAAADgHAQQAAAAcA4CCAAAADgHAQQAAACcgwACAAAAzkEAAQAAAOcggAAAAIBzEEAAAADAOQggAAAA4BwEEAAAAHAOAggAAAA4BwEEAAAAnIMAAgAAAM5BAAEAAADnIIAAAACAcxBAAAAAwDkIIAAAAOAcBBAAAABwDgIIAAAAOAcBBAAAAJyDAAIAAADOQQABAAAA5yCAAAAAgHMQQAAAAMA5CCAAAADgHAQQAAAAcA4CCAAAADgHAQQAAACcgwACAAAAzkEAAQAAAOcggAAAAIBzEEAAAADAOQggAAAA4BwEEAAAAHAOAggAAAA4BwEEAAAAnIMAAgAAAM5BAAEAAADnIIAAAACAcxBAAAAAwDkIIAAAAOCc/wcGEer3C2H6rgAAAABJRU5ErkJggg==" alt />
+<p class="caption">Rooted UPGMA tree.</p>
+</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">plot</span>(treeNJ, <span class="st">"unrooted"</span>, <span class="at">main=</span><span class="st">"NJ"</span>)</span></code></pre></div>
+<div class="figure">
+<img 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" alt />
+<p class="caption">Unrooted NJ tree.</p>
+</div>
+<div id="bootstrap" class="section level2">
+<h2>Bootstrap</h2>
+<p>To run the bootstrap we first need to write a function which computes
+a tree from an alignment. So we first need to compute a distance matrix
+and afterwards compute the tree. We can then give this function to the
+<code>bootstrap.phyDat</code> function.</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>fun <span class="ot"><-</span> <span class="cf">function</span>(x) <span class="fu">upgma</span>(<span class="fu">dist.ml</span>(x))</span>
+<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>bs_upgma <span class="ot"><-</span> <span class="fu">bootstrap.phyDat</span>(primates, fun)</span></code></pre></div>
+<p>With the new syntax of R 4.1 this can be written a bit shorter:</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>bs_upgma <span class="ot"><-</span> <span class="fu">bootstrap.phyDat</span>(primates, \(x){<span class="fu">dist.ml</span>(x) <span class="sc">|></span> upgma})</span></code></pre></div>
+<p>Finally, we can plot the tree with bootstrap values added:</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="fu">plotBS</span>(treeUPGMA, bs_upgma, <span class="at">main=</span><span class="st">"UPGMA"</span>)</span></code></pre></div>
+<div class="figure">
+<img 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" alt />
+<p class="caption">Rooted UPGMA tree.</p>
+</div>
+<p>Distance based methods are very fast and we will use the UPGMA and NJ
+tree as starting trees for the maximum parsimony and maximum likelihood
+analyses.</p>
+</div>
+</div>
+<div id="parsimony" class="section level1">
+<h1>Parsimony</h1>
+<p>The function parsimony returns the parsimony score, that is the
+minimum number of changes necessary to describe the data for a given
+tree. We can compare the parsimony score for the two trees we computed
+so far:</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="fu">parsimony</span>(treeUPGMA, primates)</span></code></pre></div>
+<pre><code>## [1] 751</code></pre>
+<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">parsimony</span>(treeNJ, primates)</span></code></pre></div>
+<pre><code>## [1] 746</code></pre>
+<p>The function most users want to use to infer phylogenies with MP
+(maximum parsimony) is <code>pratchet</code>, an implementation of the
+parsimony ratchet <span class="citation">(Nixon 1999)</span>. This
+allows to escape local optima and find better trees than only performing
+NNI / SPR rearrangements.</p>
+<p>The current implementation is</p>
+<ol style="list-style-type: decimal">
+<li>Create a bootstrap data set <span class="math inline">\(D_b\)</span>
+from the original data set.</li>
+<li>Take the current best tree and perform tree rearrangements on <span class="math inline">\(D_b\)</span> and save bootstrap tree as <span class="math inline">\(T_b\)</span>.</li>
+<li>Use <span class="math inline">\(T_b\)</span> and perform tree
+rearrangements on the original data set. If this tree has a lower
+parsimony score than the currently best tree, replace it.</li>
+<li>Iterate 1:3 until either a given number of iteration is reached
+(<code>minit</code>) or no improvements have been recorded for a number
+of iterations (<code>k</code>).</li>
+</ol>
+<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>treeRatchet <span class="ot"><-</span> <span class="fu">pratchet</span>(primates, <span class="at">trace =</span> <span class="dv">0</span>, <span class="at">minit=</span><span class="dv">100</span>)</span>
+<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="fu">parsimony</span>(treeRatchet, primates)</span></code></pre></div>
+<pre><code>## [1] 746</code></pre>
+<p>Here we set the minimum iteration of the parsimony ratchet
+(<code>minit</code>) to 100 iterations, the default number for
+<code>k</code> is 10. As the ratchet implicitly performs bootstrap
+resampling, we already computed some branch support, in our case with at
+least 100 bootstrap iterations. The parameter <code>trace=0</code> tells
+the function not write the current status to the console. The function
+may return several best trees, but these trees have no branch length
+assigned to them yet. Now let’s do this:</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>treeRatchet <span class="ot"><-</span> <span class="fu">acctran</span>(treeRatchet, primates)</span></code></pre></div>
+<p>After assigning edge weights, we prune away internal edges of length
+<code>tol</code> (default = 1e-08), so our trees may contain
+multifurcations.</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>treeRatchet <span class="ot"><-</span> <span class="fu">di2multi</span>(treeRatchet)</span></code></pre></div>
+<p>Some trees might have differed only between edges of length 0.</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="cf">if</span>(<span class="fu">inherits</span>(treeRatchet, <span class="st">"multiPhylo"</span>)){</span>
+<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a> treeRatchet <span class="ot"><-</span> <span class="fu">unique</span>(treeRatchet)</span>
+<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>}</span></code></pre></div>
+<p>As mentioned above, the parsimony ratchet implicitly performs a
+bootstrap analysis (step 1). We make use of this and store the trees
+which where visited. This allows us to add bootstrap support values to
+the tree.</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">plotBS</span>(<span class="fu">midpoint</span>(treeRatchet), <span class="at">type=</span><span class="st">"phylogram"</span>)</span>
+<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a><span class="fu">add.scale.bar</span>()</span></code></pre></div>
+<p><img 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" /><!-- --></p>
+<p>If <code>treeRatchet</code> is a list of trees, i.e. an object of
+class <code>multiPhylo</code>, we can subset the i-th trees with
+<code>treeRatchet[[i]]</code>.</p>
+<p>While in most cases <code>pratchet</code> will be enough to use,
+<code>phangorn</code> exports some function which might be useful.
+<code>random.addition</code> computes random addition and can be used to
+generate starting trees. The function <code>optim.parsimony</code>
+performs tree rearrangements to find trees with a lower parsimony score.
+The tree rearrangements implemented are nearest-neighbor interchanges
+(NNI) and subtree pruning and regrafting (SPR). The latter so far only
+works with the fitch algorithm.</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>treeRA <span class="ot"><-</span> <span class="fu">random.addition</span>(primates)</span>
+<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>treeSPR <span class="ot"><-</span> <span class="fu">optim.parsimony</span>(treeRA, primates)</span></code></pre></div>
+<pre><code>## Final p-score 746 after 0 nni operations</code></pre>
+<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">parsimony</span>(<span class="fu">c</span>(treeRA, treeSPR), primates)</span></code></pre></div>
+<pre><code>## [1] 746 746</code></pre>
+<div id="branch-and-bound" class="section level2">
+<h2>Branch and bound</h2>
+<p>For data sets with few species it is also possible to find all most
+parsimonious trees using a branch and bound algorithm <span class="citation">(Hendy and Penny 1982)</span>. For data sets with more
+than 10 taxa this can take a long time and depends strongly on how
+“tree-like” the data is. And for more than 20-30 taxa this will take
+almost forever.</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>(trees <span class="ot"><-</span> <span class="fu">bab</span>(primates[<span class="dv">1</span><span class="sc">:</span><span class="dv">10</span>,]))</span></code></pre></div>
+<pre><code>## 1 phylogenetic tree</code></pre>
+</div>
+</div>
+<div id="maximum-likelihood" class="section level1">
+<h1>Maximum likelihood</h1>
+<p>The last method we will describe in this vignette is Maximum
+Likelihood (ML) as introduced by Felsenstein <span class="citation">(Felsenstein 1981)</span>.</p>
+<div id="model-selection" class="section level2">
+<h2>Model selection</h2>
+<p>Usually, as a first step, we will try to find the best fitting model.
+For this we use the function <code>modelTest</code> to compare different
+nucleotide or protein models with the AIC, AICc or BIC, similar to
+popular programs ModelTest and ProtTest <span class="citation">(D.
+Posada and Crandall 1998; David Posada 2008; Abascal, Zardoya, and
+Posada 2005)</span>. By default available nucleotide or amino acid
+models are compared.</p>
+<p>The Vignette <em>Markov models and transition rate matrices</em>
+gives further background on those models, how they are estimated and how
+you can work with them.</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>mt <span class="ot"><-</span> <span class="fu">modelTest</span>(primates)</span></code></pre></div>
+<p>It’s also possible to only select some common models:</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>mt <span class="ot"><-</span> <span class="fu">modelTest</span>(primates, <span class="at">model=</span><span class="fu">c</span>(<span class="st">"JC"</span>, <span class="st">"F81"</span>, <span class="st">"K80"</span>, <span class="st">"HKY"</span>, <span class="st">"SYM"</span>, <span class="st">"GTR"</span>), </span>
+<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a> <span class="at">control =</span> <span class="fu">pml.control</span>(<span class="at">trace =</span> <span class="dv">0</span>))</span></code></pre></div>
+<p>The results of <code>modelTest</code> is illustrated in following
+table:</p>
+<table>
+<thead>
+<tr class="header">
+<th align="left">Model</th>
+<th align="right">df</th>
+<th align="right">logLik</th>
+<th align="right">AIC</th>
+<th align="right">AICw</th>
+<th align="right">AICc</th>
+<th align="right">AICcw</th>
+<th align="right">BIC</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">JC</td>
+<td align="right">25</td>
+<td align="right">-3068</td>
+<td align="right">6187</td>
+<td align="right">0.00</td>
+<td align="right">6193</td>
+<td align="right">0.00</td>
+<td align="right">6273</td>
+</tr>
+<tr class="even">
+<td align="left">JC+I</td>
+<td align="right">26</td>
+<td align="right">-3063</td>
+<td align="right">6177</td>
+<td align="right">0.00</td>
+<td align="right">6184</td>
+<td align="right">0.00</td>
+<td align="right">6267</td>
+</tr>
+<tr class="odd">
+<td align="left">JC+G(4)</td>
+<td align="right">26</td>
+<td align="right">-3067</td>
+<td align="right">6186</td>
+<td align="right">0.00</td>
+<td align="right">6193</td>
+<td align="right">0.00</td>
+<td align="right">6275</td>
+</tr>
+<tr class="even">
+<td align="left">JC+G(4)+I</td>
+<td align="right">27</td>
+<td align="right">-3063</td>
+<td align="right">6179</td>
+<td align="right">0.00</td>
+<td align="right">6187</td>
+<td align="right">0.00</td>
+<td align="right">6272</td>
+</tr>
+<tr class="odd">
+<td align="left">F81</td>
+<td align="right">28</td>
+<td align="right">-2918</td>
+<td align="right">5892</td>
+<td align="right">0.00</td>
+<td align="right">5900</td>
+<td align="right">0.00</td>
+<td align="right">5989</td>
+</tr>
+<tr class="even">
+<td align="left">F81+I</td>
+<td align="right">29</td>
+<td align="right">-2909</td>
+<td align="right">5876</td>
+<td align="right">0.00</td>
+<td align="right">5885</td>
+<td align="right">0.00</td>
+<td align="right">5976</td>
+</tr>
+<tr class="odd">
+<td align="left">F81+G(4)</td>
+<td align="right">29</td>
+<td align="right">-2913</td>
+<td align="right">5883</td>
+<td align="right">0.00</td>
+<td align="right">5892</td>
+<td align="right">0.00</td>
+<td align="right">5983</td>
+</tr>
+<tr class="even">
+<td align="left">F81+G(4)+I</td>
+<td align="right">30</td>
+<td align="right">-2909</td>
+<td align="right">5877</td>
+<td align="right">0.00</td>
+<td align="right">5886</td>
+<td align="right">0.00</td>
+<td align="right">5980</td>
+</tr>
+<tr class="odd">
+<td align="left">K80</td>
+<td align="right">26</td>
+<td align="right">-2953</td>
+<td align="right">5958</td>
+<td align="right">0.00</td>
+<td align="right">5965</td>
+<td align="right">0.00</td>
+<td align="right">6048</td>
+</tr>
+<tr class="even">
+<td align="left">K80+I</td>
+<td align="right">27</td>
+<td align="right">-2945</td>
+<td align="right">5943</td>
+<td align="right">0.00</td>
+<td align="right">5950</td>
+<td align="right">0.00</td>
+<td align="right">6036</td>
+</tr>
+<tr class="odd">
+<td align="left">K80+G(4)</td>
+<td align="right">27</td>
+<td align="right">-2945</td>
+<td align="right">5944</td>
+<td align="right">0.00</td>
+<td align="right">5951</td>
+<td align="right">0.00</td>
+<td align="right">6037</td>
+</tr>
+<tr class="even">
+<td align="left">K80+G(4)+I</td>
+<td align="right">28</td>
+<td align="right">-2942</td>
+<td align="right">5941</td>
+<td align="right">0.00</td>
+<td align="right">5949</td>
+<td align="right">0.00</td>
+<td align="right">6037</td>
+</tr>
+<tr class="odd">
+<td align="left">HKY</td>
+<td align="right">29</td>
+<td align="right">-2625</td>
+<td align="right">5308</td>
+<td align="right">0.00</td>
+<td align="right">5316</td>
+<td align="right">0.00</td>
+<td align="right">5408</td>
+</tr>
+<tr class="even">
+<td align="left">HKY+I</td>
+<td align="right">30</td>
+<td align="right">-2621</td>
+<td align="right">5302</td>
+<td align="right">0.00</td>
+<td align="right">5311</td>
+<td align="right">0.00</td>
+<td align="right">5406</td>
+</tr>
+<tr class="odd">
+<td align="left">HKY+G(4)</td>
+<td align="right">30</td>
+<td align="right">-2613</td>
+<td align="right">5285</td>
+<td align="right">0.18</td>
+<td align="right">5294</td>
+<td align="right">0.46</td>
+<td align="right">5389</td>
+</tr>
+<tr class="even">
+<td align="left">HKY+G(4)+I</td>
+<td align="right">31</td>
+<td align="right">-2612</td>
+<td align="right">5287</td>
+<td align="right">0.08</td>
+<td align="right">5297</td>
+<td align="right">0.14</td>
+<td align="right">5394</td>
+</tr>
+<tr class="odd">
+<td align="left">SYM</td>
+<td align="right">30</td>
+<td align="right">-2814</td>
+<td align="right">5688</td>
+<td align="right">0.00</td>
+<td align="right">5697</td>
+<td align="right">0.00</td>
+<td align="right">5791</td>
+</tr>
+<tr class="even">
+<td align="left">SYM+I</td>
+<td align="right">31</td>
+<td align="right">-2812</td>
+<td align="right">5685</td>
+<td align="right">0.00</td>
+<td align="right">5695</td>
+<td align="right">0.00</td>
+<td align="right">5792</td>
+</tr>
+<tr class="odd">
+<td align="left">SYM+G(4)</td>
+<td align="right">31</td>
+<td align="right">-2805</td>
+<td align="right">5671</td>
+<td align="right">0.00</td>
+<td align="right">5681</td>
+<td align="right">0.00</td>
+<td align="right">5778</td>
+</tr>
+<tr class="even">
+<td align="left">SYM+G(4)+I</td>
+<td align="right">32</td>
+<td align="right">-2805</td>
+<td align="right">5673</td>
+<td align="right">0.00</td>
+<td align="right">5684</td>
+<td align="right">0.00</td>
+<td align="right">5784</td>
+</tr>
+<tr class="odd">
+<td align="left">GTR</td>
+<td align="right">33</td>
+<td align="right">-2618</td>
+<td align="right">5303</td>
+<td align="right">0.00</td>
+<td align="right">5314</td>
+<td align="right">0.00</td>
+<td align="right">5417</td>
+</tr>
+<tr class="even">
+<td align="left">GTR+I</td>
+<td align="right">34</td>
+<td align="right">-2614</td>
+<td align="right">5295</td>
+<td align="right">0.00</td>
+<td align="right">5307</td>
+<td align="right">0.00</td>
+<td align="right">5412</td>
+</tr>
+<tr class="odd">
+<td align="left">GTR+G(4)</td>
+<td align="right">34</td>
+<td align="right">-2608</td>
+<td align="right">5283</td>
+<td align="right">0.47</td>
+<td align="right">5295</td>
+<td align="right">0.29</td>
+<td align="right">5400</td>
+</tr>
+<tr class="even">
+<td align="left">GTR+G(4)+I</td>
+<td align="right">35</td>
+<td align="right">-2607</td>
+<td align="right">5284</td>
+<td align="right">0.27</td>
+<td align="right">5297</td>
+<td align="right">0.11</td>
+<td align="right">5405</td>
+</tr>
+</tbody>
+</table>
+<p>To speed computations up the thresholds for the optimizations in
+<code>modelTest</code> are not as strict as for <code>optim.pml</code>
+(shown in the coming vignettes) and no tree rearrangements are
+performed, which is the most time consuming part of the optimizing
+process. As <code>modelTest</code> computes and optimizes a lot of
+models it would be a waste of computer time not to save these results.
+The results are saved as call together with the optimized trees in an
+environment and the function <code>as.pml</code> evaluates this call to
+get a <code>pml</code> object back to use for further optimization or
+analysis. This can either be done for a specific model, or for a
+specific criterion.</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>fit <span class="ot"><-</span> <span class="fu">as.pml</span>(mt, <span class="st">"HKY+G(4)+I"</span>)</span>
+<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>fit <span class="ot"><-</span> <span class="fu">as.pml</span>(mt, <span class="st">"BIC"</span>)</span></code></pre></div>
+</div>
+<div id="conducting-a-ml-tree" class="section level2">
+<h2>Conducting a ML tree</h2>
+<p>To simplify the workflow, we can give the result of
+<code>modelTest</code> to the function <code>pml_bb</code> and optimize
+the parameters taking the best model according to BIC. Ultrafast
+bootstrapping <span class="citation">(Minh, Nguyen, and Haeseler
+2013)</span> is conducted automatically if the default
+<code>rearrangements="stochastic"</code> is used. If
+<code>rearrangements="NNI"</code> is used, no bootstrapping is
+conducted.</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>fit_mt <span class="ot"><-</span> <span class="fu">pml_bb</span>(mt, <span class="at">control =</span> <span class="fu">pml.control</span>(<span class="at">trace =</span> <span class="dv">0</span>))</span>
+<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>fit_mt</span></code></pre></div>
+<pre><code>## model: HKY+G(4)
+## loglikelihood: -2615
+## unconstrained loglikelihood: -1230
+## Discrete gamma model
+## Number of rate categories: 4
+## Shape parameter: 2.3
+##
+## Rate matrix:
+## a c g t
+## a 0 1 55 1
+## c 1 0 1 55
+## g 55 1 0 1
+## t 1 55 1 0
+##
+## Base frequencies:
+## a c g t
+## 0.375 0.402 0.039 0.184</code></pre>
+<p>We can also use <code>pml_bb</code> with a defined model to infer a
+phylogenetic tree.</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>fitGTR <span class="ot"><-</span> <span class="fu">pml_bb</span>(primates, <span class="at">model=</span><span class="st">"GTR+G(4)+I"</span>)</span></code></pre></div>
+</div>
+<div id="bootstrap-1" class="section level2">
+<h2>Bootstrap</h2>
+<p>If we instead want to conduct standard bootstrapping <span class="citation">(Felsenstein 1985; Penny and Hendy 1985)</span>, we can
+do so with the function <code>bootstrap.pml</code>:</p>
+<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>bs <span class="ot"><-</span> <span class="fu">bootstrap.pml</span>(fit_mt, <span class="at">bs=</span><span class="dv">100</span>, <span class="at">optNni=</span><span class="cn">TRUE</span>,</span>
+<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a> <span class="at">control =</span> <span class="fu">pml.control</span>(<span class="at">trace =</span> <span class="dv">0</span>))</span></code></pre></div>
+<p>Now we can plot the tree with the bootstrap support values on the
+edges and compare the standard bootstrap values to the ultrafast
+bootstrap values. With the function <code>plotBS</code> it is not only
+possible to plot these two, but also the transfer bootstraps <span class="citation">(Lemoine et al. 2018)</span> which are especially
+useful for large data sets.</p>
+<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">plotBS</span>(<span class="fu">midpoint</span>(fit_mt<span class="sc">$</span>tree), <span class="at">p =</span> .<span class="dv">5</span>, <span class="at">type=</span><span class="st">"p"</span>, <span class="at">digits=</span><span class="dv">2</span>, <span class="at">main=</span><span class="st">"Ultrafast bootstrap"</span>)</span>
+<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plotBS</span>(<span class="fu">midpoint</span>(fit_mt<span class="sc">$</span>tree), bs, <span class="at">p =</span> <span class="dv">50</span>, <span class="at">type=</span><span class="st">"p"</span>, <span class="at">main=</span><span class="st">"Standard bootstrap"</span>)</span>
+<span id="cb31-4"><a href="#cb31-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb31-5"><a href="#cb31-5" aria-hidden="true" tabindex="-1"></a><span class="fu">plotBS</span>(<span class="fu">midpoint</span>(fit_mt<span class="sc">$</span>tree), bs, <span class="at">p =</span> <span class="dv">50</span>, <span class="at">type=</span><span class="st">"p"</span>, <span class="at">digits=</span><span class="dv">0</span>, <span class="at">method =</span> <span class="st">"TBE"</span>, <span class="at">main=</span><span class="st">"Transfer bootstrap"</span>)</span></code></pre></div>
+<div class="figure">
+<img 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" alt="Unrooted tree (midpoint rooted) with ultrafast, standard and transfer bootstrap support values." width="33%" /><img src="data:image/png;base64,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" alt="Unrooted tree (midpoint rooted) with ultrafast, standard and transfer bootstrap support values." width="33%" /><img 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" alt="Unrooted tree (midpoint rooted) with ultrafast, standard and transfer bootstrap support values." width="33%" />
+<p class="caption">
+Unrooted tree (midpoint rooted) with ultrafast, standard and transfer
+bootstrap support values.
+</p>
+</div>
+<p>If we want to assign the standard or transfer bootstrap values to the
+node labels in our tree instead of plotting it (e.g. to export the tree
+somewhere else), <code>plotBS</code> gives that option with
+<code>type = "n"</code>:</p>
+<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="co"># assigning standard bootstrap values to our tree; this is the default method</span></span>
+<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>tree_stdbs <span class="ot"><-</span> <span class="fu">plotBS</span>(fit_mt<span class="sc">$</span>tree, bs, <span class="at">type =</span> <span class="st">"n"</span>)</span>
+<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb32-4"><a href="#cb32-4" aria-hidden="true" tabindex="-1"></a><span class="co"># assigning transfer bootstrap values to our tree</span></span>
+<span id="cb32-5"><a href="#cb32-5" aria-hidden="true" tabindex="-1"></a>tree_tfbs <span class="ot"><-</span> <span class="fu">plotBS</span>(fit_mt<span class="sc">$</span>tree, bs, <span class="at">type =</span> <span class="st">"n"</span>, <span class="at">method =</span> <span class="st">"TBE"</span>)</span></code></pre></div>
+<p>It is also possible to look at <code>consensusNet</code> to identify
+potential conflict.</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>cnet <span class="ot"><-</span> <span class="fu">consensusNet</span>(bs, <span class="at">p=</span><span class="fl">0.2</span>)</span>
+<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(cnet, <span class="at">show.edge.label=</span><span class="cn">TRUE</span>)</span></code></pre></div>
+<div class="figure">
+<img src="data:image/png;base64,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" alt />
+<p class="caption">ConsensusNet from the standard bootstrap sample.</p>
+</div>
+<p>Several analyses, e.g.<code>bootstrap</code> and
+<code>modelTest</code>, can be computationally demanding, but as
+nowadays most computers have several cores, one can distribute the
+computations using the <em>parallel</em> package. However, it is only
+possible to use this approach if R is running from command line (“X11”),
+but not using a GUI (for example “Aqua” on Macs) and unfortunately the
+<em>parallel</em> package does not work at all under Windows.</p>
+</div>
+<div id="exporting-a-tree" class="section level2">
+<h2>Exporting a tree</h2>
+<p>Now that we have our tree with bootstrap values, we can easily write
+it to a file in <em>Newick</em>-format:</p>
+<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a><span class="co"># tree with ultrafast bootstrap values</span></span>
+<span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a><span class="fu">write.tree</span>(fit_mt<span class="sc">$</span>tree, <span class="st">"primates.tree"</span>)</span>
+<span id="cb34-3"><a href="#cb34-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb34-4"><a href="#cb34-4" aria-hidden="true" tabindex="-1"></a><span class="co"># tree with standard bootstrap values</span></span>
+<span id="cb34-5"><a href="#cb34-5" aria-hidden="true" tabindex="-1"></a><span class="fu">write.tree</span>(tree_stdbs, <span class="st">"primates.tree"</span>)</span>
+<span id="cb34-6"><a href="#cb34-6" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb34-7"><a href="#cb34-7" aria-hidden="true" tabindex="-1"></a><span class="co"># tree with transfer bootstrap values</span></span>
+<span id="cb34-8"><a href="#cb34-8" aria-hidden="true" tabindex="-1"></a><span class="fu">write.tree</span>(tree_tfbs, <span class="st">"primates.tree"</span>)</span></code></pre></div>
+</div>
+<div id="molecular-dating-with-a-strict-clock-for-ultrametric-and-tipdated-phylogenies" class="section level2">
+<h2>Molecular dating with a strict clock for ultrametric and tipdated
+phylogenies</h2>
+<p>When we assume a “molecular clock” phylogenies can be used to infer
+divergence times <span class="citation">(Zuckerkandl and Pauling
+1965)</span>. We implemented a strict clock as described in <span class="citation">(Felsenstein 2004)</span>, p. 266, allowing to infer
+ultrametric and tip-dated phylogenies. We need a starting tree that
+fulfills the assumptions, so either the tree has to be ultrametric, or
+the constraints given by the tip dates.<br />
+For an ultrametric starting tree we can use an UPGMA or WPGMA tree.</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>fit_strict <span class="ot"><-</span> <span class="fu">pml_bb</span>(primates, <span class="at">model=</span><span class="st">"HKY+G(4)"</span>, <span class="at">method=</span><span class="st">"ultrametric"</span>,</span>
+<span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a> <span class="at">rearrangement=</span><span class="st">"NNI"</span>, <span class="at">control =</span> <span class="fu">pml.control</span>(<span class="at">trace =</span> <span class="dv">0</span>))</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" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(fit_strict)</span></code></pre></div>
+<p><img 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" /><!-- --></p>
+<p>With <em>phangorn</em> we also can estimate tipdated phylogenies.
+Here we use a H3N2 virus data set from <em>treetime</em> <span class="citation">(Sagulenko, Puller, and Neher 2018)</span> as an
+example. Additionally to the alignment we also need to read in data
+containing the dates of the tips.</p>
+<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a>fdir <span class="ot"><-</span> <span class="fu">system.file</span>(<span class="st">"extdata/trees"</span>, <span class="at">package =</span> <span class="st">"phangorn"</span>)</span>
+<span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a>tmp <span class="ot"><-</span> <span class="fu">read.csv</span>(<span class="fu">file.path</span>(fdir,<span class="st">"H3N2_NA_20.csv"</span>))</span>
+<span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a>H3N2 <span class="ot"><-</span> <span class="fu">read.phyDat</span>(<span class="fu">file.path</span>(fdir,<span class="st">"H3N2_NA_20.fasta"</span>), <span class="at">format=</span><span class="st">"fasta"</span>)</span></code></pre></div>
+<p>We first process the sampling dates and create a named vector. The
+<em>lubridate</em> package <span class="citation">(Grolemund and Wickham
+2011)</span> comes in very handy dates in case one has to recode dates,
+e.g. days and months.</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>dates <span class="ot"><-</span> <span class="fu">setNames</span>(tmp<span class="sc">$</span>numdate_given, tmp<span class="sc">$</span>name)</span>
+<span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(dates)</span></code></pre></div>
+<pre><code>## A/Hawaii/02/2013|KF789866|05/28/2013|USA|12_13|H3N2/1-1409
+## 2013
+## A/Boston/DOA2_107/2012|CY148382|11/01/2012|USA|12_13|H3N2/1-1409
+## 2013
+## A/Oregon/15/2009|GQ895004|06/25/2009|USA|08_09|H3N2/1-1409
+## 2009
+## A/Hong_Kong/H090_695_V10/2009|CY115546|07/10/2009|Hong_Kong||H3N2/8-1416
+## 2010
+## A/New_York/182/2000|CY001279|02/18/2000|USA|99_00|H3N2/1-1409
+## 2000
+## A/Canterbury/58/2000|CY009150|09/05/2000|New_Zealand||H3N2/8-1416
+## 2001</code></pre>
+<p>Again we use the <code>pml_bb</code> function, which optimizes the
+tree given the constraints of the <code>tip.dates</code> vector.</p>
+<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>fit_td <span class="ot"><-</span> <span class="fu">pml_bb</span>(H3N2, <span class="at">model=</span><span class="st">"GTR+G(4)"</span>, <span class="at">method=</span><span class="st">"tipdated"</span>, <span class="at">tip.dates=</span>dates, </span>
+<span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a> <span class="at">rearrangement=</span><span class="st">"NNI"</span>, <span class="at">control =</span> <span class="fu">pml.control</span>(<span class="at">trace =</span> <span class="dv">0</span>))</span></code></pre></div>
+<p>And at last we plot the tree with a timescale.</p>
+<div class="sourceCode" id="cb41"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a>tree_td <span class="ot"><-</span> fit_td<span class="sc">$</span>tree</span>
+<span id="cb41-2"><a href="#cb41-2" aria-hidden="true" tabindex="-1"></a>root_time <span class="ot"><-</span> <span class="fu">max</span>(dates) <span class="sc">-</span> <span class="fu">max</span>(<span class="fu">node.depth.edgelength</span>(tree_td))</span>
+<span id="cb41-3"><a href="#cb41-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(tree_td, <span class="at">show.tip.label =</span> <span class="cn">FALSE</span>)</span>
+<span id="cb41-4"><a href="#cb41-4" aria-hidden="true" tabindex="-1"></a><span class="fu">axisPhylo</span>(<span class="at">root.time =</span> root_time, <span class="at">backward =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,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" /><!-- --></p>
+<p>While the loglikelihood is lower than for an unrooted tree, we have
+to keep in mind that rooted trees use less parameters. In unrooted trees
+we estimate one edge length parameter for each tree, for ultrametric
+trees we only estimate a parameter for each internal node and for
+tipdated trees we have one additional parameter for the rate.</p>
+<div style="page-break-after: always;"></div>
+</div>
+</div>
+<div id="session-info" class="section level1 unnumbered">
+<h1 class="unnumbered">Session info</h1>
+<pre><code>## R version 4.2.2 Patched (2022-11-10 r83330)
+## Platform: x86_64-pc-linux-gnu (64-bit)
+## Running under: Debian GNU/Linux bookworm/sid
+##
+## Matrix products: default
+## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0
+## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
+##
+## locale:
+## [1] C
+##
+## attached base packages:
+## [1] stats graphics grDevices utils datasets methods base
+##
+## other attached packages:
+## [1] knitr_1.41 phangorn_2.12.0.900 ape_5.6-2
+##
+## loaded via a namespace (and not attached):
+## [1] igraph_1.3.5 Rcpp_1.0.10 magrittr_2.0.3 lattice_0.20-45
+## [5] R6_2.5.1 quadprog_1.5-8 rlang_1.0.6 fastmatch_1.1-3
+## [9] fastmap_1.1.0 highr_0.10 stringr_1.5.0 tools_4.2.2
+## [13] parallel_4.2.2 grid_4.2.2 nlme_3.1-161 xfun_0.36
+## [17] cli_3.6.0 jquerylib_0.1.4 htmltools_0.5.4 yaml_2.3.6
+## [21] digest_0.6.31 lifecycle_1.0.3 Matrix_1.5-3 codetools_0.2-18
+## [25] sass_0.4.4 vctrs_0.5.1 glue_1.6.2 cachem_1.0.6
+## [29] evaluate_0.20 rmarkdown_2.20 stringi_1.7.12 compiler_4.2.2
+## [33] bslib_0.4.2 generics_0.1.3 jsonlite_1.8.4 pkgconfig_2.0.3</code></pre>
+</div>
+<div id="references" class="section level1 unnumbered">
+<h1 class="unnumbered">References</h1>
+<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="ref-Abascal2005" class="csl-entry">
+Abascal, Federico, Rafael Zardoya, and David Posada. 2005.
+<span>“ProtTest: Selection of Best-Fit Models of Protein
+Evolution.”</span> <em>Bioinformatics</em> 21 (9): 2104–5. <a href="https://doi.org/10.1093/bioinformatics/bti263">https://doi.org/10.1093/bioinformatics/bti263</a>.
+</div>
+<div id="ref-Felsenstein1981" class="csl-entry">
+Felsenstein, Joseph. 1981. <span>“Evolutionary Trees from DNA Sequences:
+A Maxumum Likelihood Approach.”</span> <em>Journal of Molecular
+Evolution</em> 17: 368–76.
+</div>
+<div id="ref-Felsenstein1985" class="csl-entry">
+———. 1985. <span>“Confidence Limits on Phylogenies. An Approach Using
+the Bootstrap.”</span> <em>Evolution</em> 39: 783–91.
+</div>
+<div id="ref-Felsenstein2004" class="csl-entry">
+———. 2004. <em>Inferring Phylogenies</em>. Sunderland: Sinauer
+Associates.
+</div>
+<div id="ref-lubridate" class="csl-entry">
+Grolemund, Garrett, and Hadley Wickham. 2011. <span>“Dates and Times
+Made Easy with <span class="nocase">lubridate</span>.”</span>
+<em>Journal of Statistical Software</em> 40 (3): 1–25. <a href="https://www.jstatsoft.org/v40/i03/">https://www.jstatsoft.org/v40/i03/</a>.
+</div>
+<div id="ref-Hendy1982" class="csl-entry">
+Hendy, M. D., and D. Penny. 1982. <span>“Branch and Bound Algorithms to
+Determine Minimal Evolutionary Trees.”</span> <em>Math. Biosc.</em> 59:
+277–90.
+</div>
+<div id="ref-Lemoine2018" class="csl-entry">
+Lemoine, Fréderic, J-B Domelevo Entfellner, Eduan Wilkinson, Damien
+Correia, M Dávila Felipe, Tulio De Oliveira, and Olivier Gascuel. 2018.
+<span>“Renewing Felsenstein’s Phylogenetic Bootstrap in the Era of Big
+Data.”</span> <em>Nature</em> 556 (7702): 452–56.
+</div>
+<div id="ref-minh2013" class="csl-entry">
+Minh, Bui Quang, Minh Anh Thi Nguyen, and Arndt von Haeseler. 2013.
+<span>“Ultrafast Approximation for Phylogenetic Bootstrap.”</span>
+<em>Molecular Biology and Evolution</em> 30 (5): 1188–95.
+</div>
+<div id="ref-Nixon1999" class="csl-entry">
+Nixon, K. 1999. <span>“The Parsimony Ratchet, a New Method for Rapid
+Rarsimony Analysis.”</span> <em>Cladistics</em> 15: 407–14.
+</div>
+<div id="ref-Paradis2012" class="csl-entry">
+Paradis, Emmanuel. 2012. <em>Analysis of Phylogenetics and Evolution
+with r</em>. Second. New York: Springer.
+</div>
+<div id="ref-Paradis2018" class="csl-entry">
+Paradis, Emmanuel, and Klaus Schliep. 2019. <span>“Ape 5.0: An
+Environment for Modern Phylogenetics and Evolutionary Analyses in
+r.”</span> <em>Bioinformatics</em> 35 (3): 526–28. <a href="https://doi.org/10.1093/bioinformatics/bty633">https://doi.org/10.1093/bioinformatics/bty633</a>.
+</div>
+<div id="ref-Penny1985" class="csl-entry">
+Penny, D., and M. D. Hendy. 1985. <span>“Testing Methods Evolutionary
+Tree Construction.”</span> <em>Cladistics</em> 1: 266–78.
+</div>
+<div id="ref-Posada2008" class="csl-entry">
+Posada, David. 2008. <span>“<span class="nocase">jModelTest</span>:
+Phylogenetic Model Averaging.”</span> <em>Molecular Biology and
+Evolution</em> 25 (7): 1253–56. <a href="https://doi.org/10.1093/molbev/msn083">https://doi.org/10.1093/molbev/msn083</a>.
+</div>
+<div id="ref-Posada1998" class="csl-entry">
+Posada, D., and K. A. Crandall. 1998. <span>“<span>MODELTEST</span>:
+Testing the Model of <span>DNA</span> Substitution.”</span>
+<em>Bioinformatics</em> 14 (9): 817–18.
+</div>
+<div id="ref-treetime" class="csl-entry">
+Sagulenko, Pavel, Vadim Puller, and Richard A Neher. 2018.
+<span>“TreeTime: Maximum-Likelihood Phylodynamic Analysis.”</span>
+<em>Virus Evolution</em> 4 (1): vex042.
+</div>
+<div id="ref-Saitou1987" class="csl-entry">
+Saitou, N., and M. Nei. 1987. <span>“The Neighbor-Joining Method - a New
+Method for Reconstructing Phylogenetic Trees.”</span> <em>Molecular
+Biology and Evolution</em> 4 (4): 406–25.
+</div>
+<div id="ref-Schliep2011" class="csl-entry">
+Schliep, Klaus Peter. 2011. <span>“Phangorn: Phylogenetic Analysis in
+<span>R</span>.”</span> <em>Bioinformatics</em> 27 (4): 592–93. <a href="https://doi.org/10.1093/bioinformatics/btq706">https://doi.org/10.1093/bioinformatics/btq706</a>.
+</div>
+<div id="ref-Studier1988" class="csl-entry">
+Studier, J. A., and K. J. Keppler. 1988. <span>“A Note on the
+Neighbor-Joining Algorithm of Saitou and Nei.”</span> <em>Molecular
+Biology and Evolution</em> 5 (6): 729–31.
+</div>
+<div id="ref-Yang2006" class="csl-entry">
+Yang, Ziheng. 2006. <em>Computational Molecular Evolution</em>. Oxford:
+Oxford University Press.
+</div>
+<div id="ref-Zuckerkandl1965" class="csl-entry">
+Zuckerkandl, Emile, and Linus Pauling. 1965. <span>“Molecules as
+Documents of Evolutionary History.”</span> <em>Journal of Theoretical
+Biology</em> 8 (2): 357–66.
+</div>
+</div>
+</div>
+
+
+
+<!-- code folding -->
+
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+ (function () {
+ var script = document.createElement("script");
+ script.type = "text/javascript";
+ script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+ document.getElementsByTagName("head")[0].appendChild(script);
+ })();
+</script>
+
+</body>
+</html>
diff --git a/inst/tinytest/test_add_edge_length.R b/inst/tinytest/test_add_edge_length.R
new file mode 100644
index 0000000..6ac5f47
--- /dev/null
+++ b/inst/tinytest/test_add_edge_length.R
@@ -0,0 +1,17 @@
+data("Laurasiatherian")
+set.seed(123)
+trees_ultra <- bootstrap.phyDat(Laurasiatherian,
+ FUN=function(x)upgma(dist.ml(x)), bs=50)
+trees_unrooted <- bootstrap.phyDat(Laurasiatherian,
+ FUN=function(x)NJ(dist.ml(x)), bs=50)
+tree_ultra <- allCompat(trees_ultra, rooted=TRUE)
+tree_ultra_el <- add_edge_length(tree_ultra, trees_ultra, rooted=TRUE)
+tree_unrooted <- allCompat(trees_unrooted, rooted=FALSE)
+tree_unrooted_el <- add_edge_length(tree_unrooted, trees_unrooted, rooted=FALSE)
+
+expect_null(tree_ultra$edge.length)
+expect_equal(length(tree_ultra_el$edge.length), nrow(tree_ultra_el$edge))
+expect_true(is.ultrametric(tree_ultra_el))
+expect_null(tree_unrooted$edge.length)
+expect_equal(length(tree_unrooted_el$edge.length), nrow(tree_unrooted_el$edge))
+expect_false(is.ultrametric(tree_unrooted_el))
diff --git a/inst/tinytest/test_bootstrap.R b/inst/tinytest/test_bootstrap.R
index b1239e9..3959fb6 100644
--- a/inst/tinytest/test_bootstrap.R
+++ b/inst/tinytest/test_bootstrap.R
@@ -3,6 +3,8 @@ fun <- function(x) NJ(dist.hamming(x))
tree <- fun(Laurasiatherian)
bs_trees <- bootstrap.phyDat(Laurasiatherian, fun)
+expect_inherits(bs_trees, "multiPhylo")
+
tree1 <- plotBS(tree, bs_trees, type="none")
tree2 <- plotBS(tree, bs_trees, type="none", method="TBE")
@@ -18,3 +20,10 @@ expect_equal(tree1, tree3)
nnet2 <- addConfidences(nnet, bs_trees)
expect_true(is.null(attr(nnet$splits, "confidences")))
expect_false(is.null(attr(nnet2$splits, "confidences")))
+
+dat <- Laurasiatherian[sample(47, 10)] |> as.character() |> phyDat()
+fit <- pml_bb(dat, "JC", rearrangement="NNI")
+bs_trees_pml <- bootstrap.pml(fit, bs=10, rearrangement="NNI")
+expect_inherits(bs_trees_pml, "multiPhylo")
+
+
diff --git a/man/pml.control.Rd b/man/pml.control.Rd
index c1160ea..c96dd1e 100644
--- a/man/pml.control.Rd
+++ b/man/pml.control.Rd
@@ -5,7 +5,8 @@
\alias{ratchet.control}
\title{Auxiliary for Controlling Fitting}
\usage{
-pml.control(epsilon = 1e-08, maxit = 10, trace = 1, tau = 1e-08)
+pml.control(epsilon = 1e-08, maxit = 10, trace = 1, tau = 1e-08,
+ statefreq = "empirical")
ratchet.control(iter = 20L, maxit = 200L, minit = 50L, prop = 1/2,
rell = TRUE, bs = 1000L)
@@ -19,11 +20,13 @@ ratchet.control(iter = 20L, maxit = 200L, minit = 50L, prop = 1/2,
\item{tau}{minimal edge length.}
+\item{statefreq}{take "empirical" or "estimate" state frequencies.}
+
\item{iter}{Number of iterations to stop if there is no change.}
\item{minit}{Minimum number of iterations.}
-\item{prop}{Only used if \code{rearrangement=stochstic}. How many NNI moves
+\item{prop}{Only used if \code{rearrangement=stochastic}. How many NNI moves
should be added to the tree in proportion of the number of taxa.´}
\item{rell}{logical, if TRUE approximate bootstraping similar Minh et al.
diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp
index c2c38d6..4f477b6 100644
--- a/src/RcppExports.cpp
+++ b/src/RcppExports.cpp
@@ -208,25 +208,25 @@ END_RCPP
RcppExport SEXP AddOnes(SEXP, SEXP, SEXP, SEXP, SEXP);
RcppExport SEXP C_rowMin(SEXP, SEXP, SEXP);
RcppExport SEXP C_sprdist(SEXP, SEXP, SEXP);
+RcppExport SEXP dist2spectra(SEXP, SEXP, SEXP);
RcppExport SEXP FS4(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
RcppExport SEXP FS5(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
-RcppExport SEXP LogLik2(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
-RcppExport SEXP PML0(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
-RcppExport SEXP PML4(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
-RcppExport SEXP PWI(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
-RcppExport SEXP dist2spectra(SEXP, SEXP, SEXP);
RcppExport SEXP getDAD(SEXP, SEXP, SEXP, SEXP, SEXP);
-RcppExport SEXP getPM(SEXP, SEXP, SEXP, SEXP);
-RcppExport SEXP getPrep(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
RcppExport SEXP getdPM(SEXP, SEXP, SEXP, SEXP);
RcppExport SEXP getdPM2(SEXP, SEXP, SEXP, SEXP);
+RcppExport SEXP getPM(SEXP, SEXP, SEXP, SEXP);
+RcppExport SEXP getPrep(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
RcppExport SEXP grpDupAtomMat(SEXP, SEXP, SEXP);
RcppExport SEXP invSites(SEXP, SEXP, SEXP, SEXP, SEXP);
RcppExport SEXP ll_free2();
RcppExport SEXP ll_init2(SEXP, SEXP, SEXP, SEXP);
+RcppExport SEXP LogLik2(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
RcppExport SEXP optE(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
RcppExport SEXP optQrtt(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
+RcppExport SEXP PML0(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
+RcppExport SEXP PML4(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
RcppExport SEXP pNodes(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
+RcppExport SEXP PWI(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
RcppExport SEXP rowMax(SEXP, SEXP, SEXP);
RcppExport SEXP sankoff3(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
RcppExport SEXP sankoff3B(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
@@ -255,25 +255,25 @@ static const R_CallMethodDef CallEntries[] = {
{"AddOnes", (DL_FUNC) &AddOnes, 5},
{"C_rowMin", (DL_FUNC) &C_rowMin, 3},
{"C_sprdist", (DL_FUNC) &C_sprdist, 3},
+ {"dist2spectra", (DL_FUNC) &dist2spectra, 3},
{"FS4", (DL_FUNC) &FS4, 15},
{"FS5", (DL_FUNC) &FS5, 11},
- {"LogLik2", (DL_FUNC) &LogLik2, 10},
- {"PML0", (DL_FUNC) &PML0, 14},
- {"PML4", (DL_FUNC) &PML4, 15},
- {"PWI", (DL_FUNC) &PWI, 6},
- {"dist2spectra", (DL_FUNC) &dist2spectra, 3},
{"getDAD", (DL_FUNC) &getDAD, 5},
- {"getPM", (DL_FUNC) &getPM, 4},
- {"getPrep", (DL_FUNC) &getPrep, 6},
{"getdPM", (DL_FUNC) &getdPM, 4},
{"getdPM2", (DL_FUNC) &getdPM2, 4},
+ {"getPM", (DL_FUNC) &getPM, 4},
+ {"getPrep", (DL_FUNC) &getPrep, 6},
{"grpDupAtomMat", (DL_FUNC) &grpDupAtomMat, 3},
{"invSites", (DL_FUNC) &invSites, 5},
{"ll_free2", (DL_FUNC) &ll_free2, 0},
{"ll_init2", (DL_FUNC) &ll_init2, 4},
+ {"LogLik2", (DL_FUNC) &LogLik2, 10},
{"optE", (DL_FUNC) &optE, 18},
{"optQrtt", (DL_FUNC) &optQrtt, 17},
+ {"PML0", (DL_FUNC) &PML0, 14},
+ {"PML4", (DL_FUNC) &PML4, 15},
{"pNodes", (DL_FUNC) &pNodes, 6},
+ {"PWI", (DL_FUNC) &PWI, 6},
{"rowMax", (DL_FUNC) &rowMax, 3},
{"sankoff3", (DL_FUNC) &sankoff3, 8},
{"sankoff3B", (DL_FUNC) &sankoff3B, 10},
Debdiff
[The following lists of changes regard files as different if they have different names, permissions or owners.]
Files in second set of .debs but not in first
-rw-r--r-- root/root /usr/lib/R/site-library/phangorn/doc/AdvancedFeatures.html -rw-r--r-- root/root /usr/lib/R/site-library/phangorn/doc/Ancestral.html -rw-r--r-- root/root /usr/lib/R/site-library/phangorn/doc/IntertwiningTreesAndNetworks.html -rw-r--r-- root/root /usr/lib/R/site-library/phangorn/doc/MLbyHand.html -rw-r--r-- root/root /usr/lib/R/site-library/phangorn/doc/Morphological.html -rw-r--r-- root/root /usr/lib/R/site-library/phangorn/doc/Networx.html -rw-r--r-- root/root /usr/lib/R/site-library/phangorn/doc/Trees.html -rw-r--r-- root/root /usr/lib/R/site-library/phangorn/tinytest/test_add_edge_length.R -rw-r--r-- root/root /usr/lib/debug/.build-id/24/fb5bc3c593b97d03a2a5dd6029737c9f926f19.debug
Files in first set of .debs but not in second
-rw-r--r-- root/root /usr/lib/debug/.build-id/7e/846b9da75420e5ef835375ce017a9bb3fce2c6.debug
No differences were encountered between the control files of package r-cran-phangorn
Control files of package r-cran-phangorn-dbgsym: lines which differ (wdiff format)
Build-Ids: 7e846b9da75420e5ef835375ce017a9bb3fce2c6 24fb5bc3c593b97d03a2a5dd6029737c9f926f19