Commit d259af52f32d599e00825428e232889d983426c5 - effects

Import Debian changes 2.0.9-1 effects (2.0.9-1) unstable; urgency=low * New upstream release * debian/control: Set (Build-)Depends: to current R version Dirk Eddelbuettel 5 years ago

10 changed file(s) with 184 addition(s) and 136 deletion(s). Raw diff Collapse all Expand all

-6

DESCRIPTION less more

0	0	Package: effects
1		Version: 2.0-8
2		Date: 2009/10/07
	1	Version: 2.0-9
	2	Date: 2009/10/11
3	3	Title: Effect Displays for Linear, Generalized Linear,
4	4	Multinomial-Logit, and Proportional-Odds Logit Models
5	5	Author: John Fox <jfox@mcmaster.ca> and Jangman Hong. We are grateful
6	6	to Robert Andersen, David Firth, and Michael Friendly, for
7		various suggestions.
	7	various suggestions, and to Ian Fellows for contributing
	8	patches.
8	9	Maintainer: John Fox <jfox@mcmaster.ca>
9	10	Depends: R (>= 2.4.0), lattice, grid, MASS, nnet, colorspace
10	11	LazyLoad: yes

16	17	URL: http://www.r-project.org, http://socserv.socsci.mcmaster.ca/jfox/
17	18	Repository: CRAN
18	19	Repository/R-Forge/Project: effects
19		Repository/R-Forge/Revision: 37
20		Date/Publication: 2009-10-09 06:55:21
21		Packaged: 2009-10-08 20:11:02 UTC; rforge
	20	Repository/R-Forge/Revision: 39
	21	Date/Publication: 2009-10-14 09:45:52
	22	Packaged: 2009-10-13 20:14:17 UTC; rforge

+44

-25

R/effects.R less more

0	0	# effect generic and methods; allEffects
1	1	# John Fox and Jangman Hong
2		# last modified 21 April 2009 by J. Fox
	2	# patches contributed by Ian Fellows 28 August 2009 (marked I-* in the sources)
	3	# last modified 11 October 2009 by J. Fox
3	4
4	5	effect <- function(term, mod, ...){
5	6	UseMethod("effect", mod)
6	7	}
7	8
8	9	effect.lm <- function (term, mod, xlevels=list(), default.levels=10, given.values,
9		se=TRUE, confidence.level=.95,
10		transformation=list(link=family(mod)$linkfun, inverse=family(mod)$linkinv),
	10	se=TRUE, confidence.level=.95,
	11	transformation=list(link=family(mod)$linkfun, inverse=family(mod)$linkinv),
11	12	typical=mean, ...){
12	13	if (missing(given.values)) given.values <- NULL
13		else if (!all(which <- names(given.values) %in% names(coef(mod))))
	14	else if (!all(which <- names(given.values) %in% names(coef(mod))))
14	15	stop("given.values (", names(given.values[!which]),") not in the model")
15	16	model.components <- analyze.model(term, mod, xlevels, default.levels)
16	17	predict.data <- model.components$predict.data

23	24	X.mod <- model.components$X.mod
24	25	cnames <- model.components$cnames
25	26	X <- model.components$X
26		formula.rhs <- formula(mod)[c(1,3)]
	27	formula.rhs <- formula(mod)[c(1,3)]
27	28	nrow.X <- nrow(X)
28		mf <- model.frame(formula.rhs, data=rbind(X[,names(predict.data),drop=FALSE], predict.data),
	29	for(i in names(predict.data)) #I-add
	30	if(is.numeric(predict.data[[i]])) X[,i] <- as.numeric(X[,i]) #I-add
	31	warn <- options(warn=-1) # calls to model.frame() and model.matrix() can generate spurious warnings
	32	mf <- model.frame(formula.rhs, data=rbind(X[,names(predict.data), drop=FALSE], predict.data),
29	33	xlev=factor.levels)
30		mod.matrix.all <- model.matrix(formula.rhs, data=mf, contrasts.arg=mod$contrasts)
	34	mod.matrix.all <- model.matrix(formula.rhs, data=mf, #I-add
	35	contrasts.arg=mod$contrasts[names(factor.levels)]) #I-add
	36	options(warn)
	37	#I-rem mod.matrix.all <- model.matrix(formula.rhs, data=mf, contrasts.arg=mod$contrasts)
31	38	mod.matrix <- mod.matrix.all[-(1:nrow.X),]
32	39	fit.1 <- na.omit(predict(mod))
33	40	wts <- mod$weights

36	43	discrepancy <- 100*sqrt(mean(mod.2$residuals^2)/mean(mod$residuals^2))
37	44	if (discrepancy > 1e-3) warning(paste("There is a discrepancy of", round(discrepancy, 3),
38	45	"percent \n in the 'safe' predictions used to generate effect", term))
39		mod.matrix <- fixup.model.matrix(mod, mod.matrix, mod.matrix.all, X.mod, mod.aug,
	46	mod.matrix <- fixup.model.matrix(mod, mod.matrix, mod.matrix.all, X.mod, mod.aug,
40	47	factor.cols, cnames, term, typical, given.values)
41	48	effect <- mod.matrix %*% mod.2$coefficients
42	49	result <- list(term=term, formula=formula(mod), response=response.name(mod),
43		variables=x, fit=effect,
44		x=predict.data[,1:n.basic, drop=FALSE], model.matrix=mod.matrix,
	50	variables=x, fit=effect,
	51	x=predict.data[,1:n.basic, drop=FALSE], model.matrix=mod.matrix,
45	52	data=X, discrepancy=discrepancy)
46	53	if (se){
47	54	if (any(family(mod)$family == c('binomial', 'poisson'))){

55	62	mod.2$terms <- mod$terms
56	63	V <- dispersion * summary.lm(mod.2)$cov
57	64	var <- diag(mod.matrix %% V %% t(mod.matrix))
58		result$se <- sqrt(var)
	65	result$se <- sqrt(var)
59	66	result$lower <- effect - z*result$se
60	67	result$upper <- effect + z*result$se
61	68	result$confidence.level <- confidence.level

69	76	result
70	77	}
71	78
72		effect.multinom <- function(term, mod,
73		confidence.level=.95, xlevels=list(), default.levels=10,
	79	effect.multinom <- function(term, mod,
	80	confidence.level=.95, xlevels=list(), default.levels=10,
74	81	given.values, se=TRUE, typical=mean, ...){
75	82	eff.mul <- function(x0){
76	83	mu <- exp(x0 %*% B)

106	113	}
107	114	if (length(mod$lev) < 3) stop("effects for multinomial logit model only available for response levels > 2")
108	115	if (missing(given.values)) given.values <- NULL
109		else if (!all(which <- colnames(given.values) %in% names(coef(mod))))
	116	else if (!all(which <- colnames(given.values) %in% names(coef(mod))))
110	117	stop("given.values (", colnames(given.values[!which]),") not in the model")
111	118	# refit model to produce 'safe' predictions when the model matrix includes
112	119	# terms -- e.g., poly(), bs() -- whose basis depends upon the data

134	141	}
135	142	X <- na.omit(X)
136	143	nrow.X <- nrow(X)
	144	for(i in names(predict.data))
	145	if(is.numeric(predict.data[[i]])) X[,i] <- as.numeric(X[,i])
137	146	data <- rbind(X[,names(newdata),drop=FALSE], newdata)
138	147	data$wt <- rep(0, nrow(data))
139	148	data$wt[1:nrow.X] <- weights(mod)
140		mod.matrix.all <- model.matrix(formula.rhs, data=data, contrasts.arg=mod$contrasts)
	149	# mod.matrix.all <- model.matrix(formula.rhs, data=data, contrasts.arg=mod$contrasts)
	150	warn <- options(warn=-1) # call to model.matrix() can generate spurious warnings
	151	mod.matrix.all <- model.matrix(formula.rhs, data=data,
	152	contrasts.arg=mod$contrasts[names(factor.levels)])
	153	options(warn)
141	154	X0 <- mod.matrix.all[-(1:nrow.X),]
142		X0 <- fixup.model.matrix(mod, X0, mod.matrix.all, X.mod, mod.aug, factor.cols,
	155	X0 <- fixup.model.matrix(mod, X0, mod.matrix.all, X.mod, mod.aug, factor.cols,
143	156	cnames, term, typical, given.values)
144	157	resp.names <- make.names(mod$lev, unique=TRUE)
145	158	resp.names <- c(resp.names[-1], resp.names[1]) # make the last level the reference level

198	211	model.matrix=X0, data=X, discrepancy=discrepancy, model="multinom",
199	212	prob=P, logit=Logit)
200	213	if (se) result <- c(result, list(se.prob=SE.P, se.logit=SE.logit,
201		lower.logit=Lower.logit, upper.logit=Upper.logit,
	214	lower.logit=Lower.logit, upper.logit=Upper.logit,
202	215	lower.prob=Lower.P, upper.prob=Upper.P,
203	216	confidence.level=confidence.level))
204	217	class(result) <-'effpoly'
205	218	result
206	219	}
207	220
208		effect.polr <- function(term, mod,
209		confidence.level=.95, xlevels=list(), default.levels=10,
	221	effect.polr <- function(term, mod,
	222	confidence.level=.95, xlevels=list(), default.levels=10,
210	223	given.values, se=TRUE, typical=mean, latent=FALSE, ...){
211	224	if (mod$method != "logistic") stop('method argument to polr must be "logistic"')
212	225	if (missing(given.values)) given.values <- NULL
213		else if (!all(which <- names(given.values) %in% names(coef(mod))))
	226	else if (!all(which <- names(given.values) %in% names(coef(mod))))
214	227	stop("given.values (", names(given.values[!which]),") not in the model")
215	228	eff.polr <- function(x0){
216	229	eta0 <- x0 %*% b

281	294	}
282	295	X <- na.omit(X)
283	296	nrow.X <- nrow(X)
	297	for(i in names(predict.data))
	298	if(is.numeric(predict.data[[i]])) X[,i] <- as.numeric(X[,i])
284	299	data <- rbind(X[,names(newdata),drop=FALSE], newdata)
285	300	wts <- mod$model[["(weights)"]]
286	301	if (is.null(wts)) wts <- 1
287	302	data$wt <- rep(0, nrow(data))
288	303	data$wt[1:nrow.X] <- wts
289		mod.matrix.all <- model.matrix(formula.rhs, data=data, contrasts.arg=mod$contrasts)
	304	# mod.matrix.all <- model.matrix(formula.rhs, data=data, contrasts.arg=mod$contrasts)
	305	warn <- options(warn=-1) # call to model.matrix() can generate spurious warnings
	306	mod.matrix.all <- model.matrix(formula.rhs, data=data,
	307	contrasts.arg=mod$contrasts[names(factor.levels)])
	308	options(warn)
290	309	X0 <- mod.matrix.all[-(1:nrow.X),]
291		X0 <- fixup.model.matrix(mod, X0, mod.matrix.all, X.mod, mod.aug, factor.cols,
	310	X0 <- fixup.model.matrix(mod, X0, mod.matrix.all, X.mod, mod.aug, factor.cols,
292	311	cnames, term, typical, given.values)
293	312	resp.names <- make.names(mod$lev, unique=TRUE)
294	313	mod <- polr(formula(mod), data=data, Hess=TRUE, weights=wt)

304	323	alpha <- - mod$zeta # intercepts are negatives of thresholds
305	324	z <- qnorm(1 - (1 - confidence.level)/2)
306	325	result <- list(term=term, formula=formula(mod), response=response.name(mod),
307		y.levels=mod$lev, variables=x,
	326	y.levels=mod$lev, variables=x,
308	327	x=predict.data[,1:n.basic, drop=FALSE],
309	328	model.matrix=X0, data=X, discrepancy=discrepancy, model="polr")
310	329	if (latent){

361	380	result$logit <- Logit
362	381	if (se) result <- c(result,
363	382	list(se.prob=SE.P, se.logit=SE.Logit,
364		lower.logit=Lower.logit, upper.logit=Upper.logit,
	383	lower.logit=Lower.logit, upper.logit=Upper.logit,
365	384	lower.prob=Lower.P, upper.prob=Upper.P,
366	385	confidence.level=confidence.level))
367	386	class(result) <-'effpoly'

388	407	all.effects <- function(...){
389	408	.Deprecated("allEffects")
390	409	allEffects(...)
391		}⏎
	410	}

+66

-60

R/utilities.R less more

0	0	# utilities and common functions for effects package
1	1	# John Fox and Jangman Hong
2		# last modified 06 May 2009 by J. Fox
3
	2	# patches contributed by Ian Fellows 28 August 2009 (marked I-* in the sources)
	3	# last modified 11 October 2009 by J. Fox
4	4
5	5	has.intercept <- function(model, ...) any(names(coefficients(model))=="(Intercept)")
6	6

26	26	f <- formula(model)
27	27	data <- eval(model$call$data, envir)
28	28	ff <- foo ~ bar + baz
29		if (is.call(extras))
	29	if (is.call(extras))
30	30	gg <- extras
31	31	else gg <- parse(text = paste("~", paste(extras, collapse = "+")))[[1]]
32	32	ff[[2]] <- f[[2]]

42	42	}
43	43	else {
44	44	subset <- model$call$subset
45		rval <- eval(call("model.frame", ff, data = data, subset = subset,
	45	rval <- eval(call("model.frame", ff, data = data, subset = subset,
46	46	na.action = I), envir)
47	47	oldmf <- model.frame(model)
48	48	keep <- match(rownames(oldmf), rownames(rval))

70	70	if (0 == length(result)) which.term else result
71	71	}
72	72	else {
73		factors <- attr(mod$terms, "factors")
	73	factors <- attr(mod$terms, "factors")
74	74	rownames(factors) <- gsub(" ", "", rownames(factors))
75		colnames(factors) <- gsub(" ", "", colnames(factors))
	75	colnames(factors) <- gsub(" ", "", colnames(factors))
76	76	result<-(1:length(names))[-which.term][sapply(names[-which.term],
77	77	function(term2) is.relative(term2, term, factors))]
78	78	if (0 == length(result)) which.term else result

146	146	}
147	147
148	148	analyze.model <- function(term, mod, xlevels, default.levels){
149		if ((!is.null(mod$na.action)) && class(mod$na.action) == "exclude")
	149	if ((!is.null(mod$na.action)) && class(mod$na.action) == "exclude")
150	150	class(mod$na.action) <- "omit"
151	151	term <- gsub(" ", "", gsub("\\*", ":", term))
152	152	intercept <- has.intercept(mod)
153	153	terms <- term.names(mod)
154	154	if (intercept) terms <- terms[-1]
155	155	which.term <- which(term==terms)
156		mod.aug<- list()
	156	mod.aug <- list()
157	157	if (length(which.term) == 0){
158	158	warning(paste(term,"does not appear in the model"))
159	159	mod.aug <- update(formula(mod), eval(parse(text=paste(". ~ . +", term))))

176	176	all.vars <- all.vars(as.formula(paste ("~", paste(terms[all.vars], collapse="+"))))
177	177	basic.vars <- all.vars(as.formula(paste ("~", paste(terms[basic.vars], collapse="+"))))
178	178	}
179		excluded.vars <- if (length(excluded.vars) > 0)
	179	excluded.vars <- if (length(excluded.vars) > 0)
180	180	all.vars(as.formula(paste ("~", paste(terms[excluded.vars], collapse="+"))))
181	181	else NULL
182	182	X.mod <- model.matrix(mod)

184	184	factor.cols <- rep(FALSE, length(cnames))
185	185	names(factor.cols) <- cnames
186	186	X <- model.frame(mod)
187		for (name in all.vars){
188		if (is.factor(X[[name]])) factor.cols[grep(paste("^", name, sep=""), cnames)] <- TRUE
189		}
190		factor.cols[grep(":", cnames)] <- FALSE
	187	##begin I-add##
	188	storType <- attr(terms(mod), "dataClasses")
	189	for (name in names(storType[storType=="factor" \| storType=="logical"])){
	190	name <- gsub("[()]", ".", gsub("\\.", "\\\\.", name))
	191	factor.cols[grep(paste("^", name, sep=""), cnames)] <- TRUE
	192	}
	193	factor.cols[grep(":", cnames,extended=FALSE)] <- FALSE
191	194	X <- na.omit(expand.model.frame(mod, all.vars))
192		x<-list()
	195	factor.levels <- list()
	196	basicType<-rep(NA,length(basic.vars))
	197	factor.vars<-c()
	198	for(factor.term in names(storType)[storType=="factor"\| storType=="logical"]){
	199	factor.vars<-unique(c(factor.vars,all.vars(as.formula(paste("~",factor.term)))))
	200	}
	201	##end I-add##
	202	#I-rem factor.cols[grep(":", cnames)] <- FALSE
	203	#I-rem X <- na.omit(expand.model.frame(mod, all.vars))
	204	x <- list()
193	205	factor.levels <- list()
194	206	for (name in basic.vars){
195	207	levels <- mod$xlevels[[name]]
196		fac <- !is.null(levels)
	208	if(is.null(levels)) levels<-levels(factor(X[,name])) #I-add
	209	fac <- name %in%factor.vars #I-add
	210	#I-rem fac <- !is.null(levels)
197	211	if (!fac) {
198	212	levels <- if (is.null(xlevels[[name]]))
199		seq(min(X[, name]), max(X[,name]), length=default.levels)
	213	seq(min(as.numeric(X[, name])), max(as.numeric(X[,name])), length=default.levels)
	214	#I-chg seq(min(X[, name]), max(X[,name]), length=default.levels)
200	215	else xlevels[[name]]
201	216	}
202		else factor.levels[[name]] <- levels
	217	else if(!is.null(levels)){ #I-add
	218	factor.levels[[name]] <- levels#I-add
	219	}else{#I-add
	220	factor.levels[[name]] <-levels(factor(X[,name]))#I-add
	221	}#I-add
	222	#I-rem else factor.levels[[name]] <- levels
203	223	x[[name]] <- list(name=name, is.factor=fac, levels=levels)
204	224	}
205	225	x.excluded <- list()
	226	##begin I-add#
206	227	for (name in excluded.vars){
207	228	levels <- mod$xlevels[[name]]
208		fac <- !is.null(levels)
209		level <- if (fac) levels[1] else mean(X[, name])
	229	fac <- name %in%factor.vars
	230	if(is.null(levels)) levels <- levels(factor(X[,name]))
	231	level <- if (fac) levels[1] else mean(as.numeric(X[, name]))
210	232	if (fac) factor.levels[[name]] <- levels
211	233	x.excluded[[name]] <- list(name=name, is.factor=fac,
212	234	level=level)
213	235	}
	236	#end I-add#
	237
	238	#I-rem#
	239	# for (name in excluded.vars){
	240	# levels <- mod$xlevels[[name]]
	241	# fac <- !is.null(levels)
	242	# if (fac) factor.levels[[name]] <- levels
	243	# x.excluded[[name]] <- list(name=name, is.factor=fac,
	244	# level=level)
	245	# }
214	246	dims <- sapply(x, function(x) length(x$levels))
215	247	len <- prod(dims)
216	248	n.basic <- length(basic.vars)

229	261	colnames(predict.data) <- c(sapply(x, function(x) x$name),
230	262	sapply(x.excluded, function(x) x$name))
231	263	predict.data <- matrix.to.df(predict.data)
232		list(predict.data=predict.data, factor.levels=factor.levels,
	264	list(predict.data=predict.data, factor.levels=factor.levels,
233	265	factor.cols=factor.cols, mod.aug=mod.aug, term=term, n.basic=n.basic,
234		x=x, X.mod=X.mod, cnames=cnames, X=X)
235		}
236
237		#fixup.model.matrix <- function(mod, mod.matrix, mod.matrix.all, X.mod, mod.aug,
238		# factor.cols, cnames, term, typical, given.values){
239		# attr(mod.matrix, "assign") <- attr(mod.matrix.all, "assign")
240		# stranger.cols <- factor.cols &
241		# apply(outer(strangers(term, mod, mod.aug), attr(mod.matrix,'assign'), '=='), 2, any)
242		# if (has.intercept(mod)) stranger.cols[1] <- TRUE
243		# if (any(stranger.cols)) {
244		# mod.matrix[,stranger.cols] <-
245		# matrix(apply(as.matrix(X.mod[,stranger.cols]), 2, typical),
246		# nrow=nrow(mod.matrix), ncol=sum(stranger.cols), byrow=TRUE)
247		# if (!is.null(given.values)){
248		# stranger.names <- names(stranger.cols[stranger.cols])
249		# given <- stranger.names %in% names(given.values)
250		# if (any(given)) mod.matrix[,stranger.names[given]] <- given.values[stranger.names[given]]
251		# }
252		# }
253		# for (name in cnames){
254		# components <- unlist(strsplit(name, ':'))
255		# if (length(components) > 1)
256		# mod.matrix[,name] <- apply(mod.matrix[,components], 1, prod)
257		# }
258		# mod.matrix
259		#}
260
261		fixup.model.matrix <- function(mod, mod.matrix, mod.matrix.all, X.mod, mod.aug,
	266	x=x, X.mod=X.mod, cnames=cnames, X=X)
	267	}
	268
	269	fixup.model.matrix <- function(mod, mod.matrix, mod.matrix.all, X.mod, mod.aug,
262	270	factor.cols, cnames, term, typical, given.values){
263	271	attr(mod.matrix, "assign") <- attr(mod.matrix.all, "assign")
264		stranger.cols <-
	272	stranger.cols <-
265	273	apply(outer(strangers(term, mod, mod.aug), attr(mod.matrix,'assign'), '=='), 2, any)
266	274	if (has.intercept(mod)) stranger.cols[1] <- TRUE
267	275	if (any(stranger.cols)) {
268	276	facs <- factor.cols & stranger.cols
269	277	covs <- (!factor.cols) & stranger.cols
270		if (any(facs)) mod.matrix[,facs] <-
271		matrix(apply(as.matrix(X.mod[,facs]), 2, mean),
	278	if (any(facs)) mod.matrix[,facs] <-
	279	matrix(apply(as.matrix(X.mod[,facs]), 2, mean),
272	280	nrow=nrow(mod.matrix), ncol=sum(facs), byrow=TRUE)
273		if (any(covs)) mod.matrix[,covs] <-
274		matrix(apply(as.matrix(X.mod[,covs]), 2, typical),
	281	if (any(covs)) mod.matrix[,covs] <-
	282	matrix(apply(as.matrix(X.mod[,covs]), 2, typical),
275	283	nrow=nrow(mod.matrix), ncol=sum(covs), byrow=TRUE)
276	284	if (!is.null(given.values)){
277	285	stranger.names <- cnames[stranger.cols]
278	286	given <- stranger.names %in% names(given.values)
279		if (any(given)) mod.matrix[,stranger.names[given]] <-
280		matrix(given.values[stranger.names[given]], nrow=nrow(mod.matrix),
281		ncol=length(stranger.names[given]), byrow=TRUE)
282		}
	287	if (any(given)) mod.matrix[,stranger.names[given]] <-
	288	matrix(given.values[stranger.names[given]], nrow=nrow(mod.matrix),
	289	ncol=length(stranger.names[given]), byrow=TRUE)
	290	}
283	291	}
284	292	for (name in cnames){
285	293	components <- unlist(strsplit(name, ':'))
286		if (length(components) > 1)
	294	if (length(components) > 1)
287	295	mod.matrix[,name] <- apply(mod.matrix[,components], 1, prod)
288	296	}
289	297	mod.matrix

324	332	grid.segments(x, unit(0, "npc"), x, unit(0.5, "lines"),
325	333	default.units="native")
326	334	}
327
328

-0

debian/changelog less more

	0	effects (2.0.9-1) unstable; urgency=low
	1
	2	* New upstream release
	3
	4	* debian/control: Set (Build-)Depends: to current R version
	5
	6	-- Dirk Eddelbuettel <edd@debian.org> Wed, 14 Oct 2009 17:28:53 -0500
	7
0	8	effects (2.0.8-1) unstable; urgency=low
1	9
2	10	* New upstream release

-2

debian/control less more

1	1	Section: gnu-r
2	2	Priority: optional
3	3	Maintainer: Dirk Eddelbuettel <edd@debian.org>
4		Build-Depends: debhelper (>> 4.1.0), r-base-dev (>= 2.9.2), cdbs, r-cran-lattice (>= 0.10.11), r-cran-vr, r-cran-colorspace
	4	Build-Depends: debhelper (>> 4.1.0), r-base-dev (>= 2.10.0~20091013), cdbs, r-cran-lattice (>= 0.10.11), r-cran-vr (> 7.2.29), r-cran-colorspace
5	5	Standards-Version: 3.8.3
6	6
7	7	Package: r-cran-effects
8	8	Architecture: all
9		Depends: r-base-core (>= 2.9.2), r-cran-lattice (>= 0.10.11), r-cran-vr, r-cran-colorspace
	9	Depends: r-base-core (>= 2.10.0~20091013), r-cran-lattice (>= 0.10.11), r-cran-vr, r-cran-colorspace
10	10	Description: GNU R graphical and tabular effects display for glm models
11	11	This package provides functions for 'effect' displays: Graphical and
12	12	tabular effect displays, e.g., of interactions, for linear generalized

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inst/CHANGES less more

117	117
118	118	o Version corresponding to John Fox, Jangman Hong (2009), Effect Displays in R for Multinomial and Proportional-Odds Logit Models: Extensions to the effects Package. Journal of Statistical Software, 32(1), 1-24 <http://www.jstatsoft.org/v32/i01/>.
119	119
120		O Fixed [pgk] markup in .Rd file cross-references.
	120	o Fixed [pgk] markup in .Rd file cross-references.
	121
	122	Version 2.0-9
	123
	124	o Applied patches contributed by Ian Fellows to allow logical predictors and various coercions in model formulas to work properly.
	125
	126	o Fixed name of CITATION file (was CITATION.txt).
	127
	128	o Small changes to docs.

+38

-0

inst/CITATION less more

	0	citHeader("To cite effects in publications use:")
	1
	2	citEntry(entry = "Article",
	3	title = "Effect Displays in {R} for Generalised Linear Models",
	4	author = as.person("John Fox"),
	5	journal = "Journal of Statistical Software",
	6	year = "2003",
	7	volume = "8",
	8	number = "15",
	9	pages = "1--27",
	10	url = "http://www.jstatsoft.org/v08/i15/",
	11
	12	textVersion =
	13	paste("John Fox (2003).",
	14	"Effect Displays in R for Generalised Linear Models.",
	15	"Journal of Statistical Software, 8(15), 1-27.",
	16	"URL http://www.jstatsoft.org/v08/i15/.")
	17	)
	18
	19	citEntry(entry = "Article",
	20	title = "Effect Displays in {R} for Multinomial and Proportional-Odds Logit Models: Extensions to the {effects} Package",
	21	author = personList(as.person("John Fox"),
	22	as.person("Jangman Hong")),
	23	journal = "Journal of Statistical Software",
	24	year = "2009",
	25	volume = "32",
	26	number = "1",
	27	pages = "1--24",
	28	url = "http://www.jstatsoft.org/v32/i01/",
	29
	30	textVersion =
	31	paste("John Fox, Jangman Hong (2009).",
	32	"Effect Displays in R for Multinomial and Proportional-Odds Logit Models: Extensions to the effects Package.",
	33	"Journal of Statistical Software, 32(1), 1-24.",
	34	"URL http://www.jstatsoft.org/v32/i01/."),
	35
	36	header = "For usage in multinomial and proportional-odds logit models also cite:"
	37	)

-38

~~inst/CITATION.txt~~ less more

0		citHeader("To cite effects in publications use:")
1
2		citEntry(entry = "Article",
3		title = "Effect Displays in {R} for Generalised Linear Models",
4		author = as.person("John Fox"),
5		journal = "Journal of Statistical Software",
6		year = "2003",
7		volume = "8",
8		number = "15",
9		pages = "1--27",
10		url = "http://www.jstatsoft.org/v08/i15/",
11
12		textVersion =
13		paste("John Fox (2003).",
14		"Effect Displays in R for Generalised Linear Models.",
15		"Journal of Statistical Software, 8(15), 1-27.",
16		"URL http://www.jstatsoft.org/v08/i15/.")
17		)
18
19		citEntry(entry = "Article",
20		title = "Effect Displays in {R} for Multinomial and Proportional-Odds Logit Models: Extensions to the {effects} Package",
21		author = personList(as.person("John Fox"),
22		as.person("Jangman Hong")),
23		journal = "Journal of Statistical Software",
24		year = "2009",
25		volume = "32",
26		number = "1",
27		pages = "1--24",
28		url = "http://www.jstatsoft.org/v32/i01/",
29
30		textVersion =
31		paste("John Fox, Jangman Hong (2009).",
32		"Effect Displays in R for Multinomial and Proportional-Odds Logit Models: Extensions to the effects Package.",
33		"Journal of Statistical Software, 32(1), 1-24.",
34		"URL http://www.jstatsoft.org/v32/i01/."),
35
36		header = "For usage in multinomial and proportional-odds logit models also cite:"
37		)

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man/effect.Rd less more

169	169	\bold{36}, 225--255.
170	170
171	171	Fox, J. and J. Hong (2009).
172		Effect displays in R for multinomial and proportional-ddds logit models:
	172	Effect displays in R for multinomial and proportional-odds logit models:
173	173	Extensions to the effects package.
174	174	\emph{Journal of Statistical Software}
175	175	\bold{32:1}, 1--24.", <\url{http://www.jstatsoft.org/v32/i01/}>.

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man/effects-package.Rd less more

14	14	\details{
15	15	\tabular{ll}{
16	16	Package: \tab effects\cr
17		Version: \tab 2.0-8\cr
18		Date: \tab 2009/10/07\cr
	17	Version: \tab 2.0-9\cr
	18	Date: \tab 2009/10/11\cr
19	19	Depends: \tab R (>= 2.4.0), lattice, grid, MASS, nnet, colorspace\cr
20	20	LazyLoad: \tab yes\cr
21	21	LazyData: \tab yes\cr

48	48
49	49	\author{
50	50	John Fox <jfox@mcmaster.ca> and Jangman Hong. We are grateful to Robert Andersen, David Firth, and
51		Michael Friendly, for various suggestions.
	51	Michael Friendly, for various suggestions, and to Ian Fellows for contributing patches..
52	52
53	53	Maintainer: John Fox <jfox@mcmaster.ca>
54	54	}

68	68	Effect displays for multinomial and proportional-odds logit models.
69	69	\emph{Sociological Methodology}
70	70	\bold{36}, 225--255.
	71
	72	Fox, J. and J. Hong (2009).
	73	Effect displays in R for multinomial and proportional-odds logit models:
	74	Extensions to the effects package.
	75	\emph{Journal of Statistical Software}
	76	\bold{32:1}, 1--24.", <\url{http://www.jstatsoft.org/v32/i01/}>.
71	77	}
72	78
73	79