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# plot, summary, and print methods for effects package
# John Fox and Jangman Hong
# last modified 2012-11-30 by J. Fox
# 29 June 2011 added grid, rotx and roty arguments to the two plot methods
# by S. Weisberg
# 21 Dec 2012 modest modification of empty cells with crossed factors
# 2013-01-17: Added factor.ci.style arg to plot.eff() and plot.effpoly(). J. Fox
# 2013-01-18: Added CI bars to multiline plots with factor.ci.style="bars"
# 2013-01-19: Renamed 'factor.ci.style' to 'ci.style'. Added a 'none' option
# extended to variate terms if multiline=TRUE, ci.style="bars"
# 2013-01-30: scale arrow "heads" for error bars relative to cex
# 2013-05-31: fixed symbol colors in legends in plot.eff(). J. Fox
# 2013-08-14: fixed bug in restoring warn option. J. Fox
# 2013-08-27: fixed symbols argument for multiline plot in plot.eff(), reported by Ulrike Gromping. J. Fox
# 2013-08-31: fixed handling of ticks.x argument. John
# 2013-09-25: moved plot.eff methods to plot.methods.R for easier work. Michael
# 2013-10-17: added use.splines argument to plot.effpoly. Sandy
# 2025-07-22: fix summary.eff when transformation is inverse rather than direct. John
summary.eff <- function(object, type=c("response", "link"), ...){
effect <- as.vector(object$fit)
trans.effect <- object$transformation$inverse(effect)
check.order <- if (all(order(effect) == order(trans.effect))){
"direct"
} else if (all(order(effect) == order(- trans.effect))){
"inverse"
} else {
"inconsistent"
}
if (check.order == "inconsistent") {
warning("the response transformation appears to be non-monotone")
}
result <- list()
result$header <- paste("\n", gsub(":", "*", object$term), 'effect\n')
result$offset <- object$offset
type <- match.arg(type)
if (type == "response") {
object$fit <- object$transformation$inverse(object$fit)
if (!is.null(object$confidence.level)){
if (check.order == "inverse"){
save.upper <- object$upper
object$upper <- object$transformation$inverse(object$lower)
object$lower <- object$transformation$inverse(save.upper)
} else {
object$lower <- object$transformation$inverse(object$lower)
object$upper <- object$transformation$inverse(object$upper)
}
}
}
result$effect <- array(object$fit,
dim=sapply(object$variables, function(x) length(x$levels)),
dimnames=lapply(object$variables, function(x) x$levels))
if (!is.null(object$se)){
result$lower.header <- paste('\n Lower', round(100*object$confidence.level, 2),
'Percent Confidence Limits\n')
result$lower <- array(object$lower,
dim=sapply(object$variables, function(x) length(x$levels)),
dimnames=lapply(object$variables, function(x) x$levels))
result$upper.header <- paste('\n Upper', round(100*object$confidence.level, 2),
'Percent Confidence Limits\n')
result$upper <- array(object$upper,
dim=sapply(object$variables, function(x) length(x$levels)),
dimnames=lapply(object$variables, function(x) x$levels))
}
if (object$discrepancy > 1e-3) result$warning <- paste("\nWarning: There is an average discrepancy of",
round(object$discrepancy, 3),
"percent \n in the 'safe' predictions for effect", object$term, '\n')
class(result) <- "summary.eff"
result
}
print.summary.eff <- function(x, ...){
cat(x$header)
if (x$offset != 0) cat("\noffset = ", x$offset, "\n\n")
print(x$effect, ...)
if (!is.null(x$lower)){
cat(x$lower.header)
print(x$lower, ...)
cat(x$upper.header)
print(x$upper, ...)
}
if (!is.null(x$thresholds)){
cat("\nThresholds:\n")
print(x$thresholds, ...)
}
if (!is.null(x$warning)) cat(x$warning)
invisible(x)
}
print.eff <- function(x, type=c("response", "link"), ...){
cat(paste("\n", gsub(":", "*", x$term), 'effect\n'))
if (x$offset != 0) cat("\noffset = ", x$offset, "\n\n")
type <- match.arg(type)
if (type == "response") x$fit <- x$transformation$inverse(x$fit)
table <- array(x$fit,
dim=sapply(x$variables, function(x) length(x$levels)),
dimnames=lapply(x$variables, function(x) x$levels))
print(table, ...)
if (x$discrepancy > 1e-3) cat(paste("\nWarning: There is an average discrepancy of",
round(x$discrepancy, 3),
"percent \n in the 'safe' predictions for effect", x$term, '\n'))
invisible(x)
}
print.efflist <- function(x, ...){
cat(" model: ")
form <- x[[1]]$formula
attributes(form) <- NULL
print(form)
for (effect in names(x)){
print(x[[effect]], ...)
}
invisible(x)
}
summary.efflist <- function(object, ...){
cat(" model: ")
form <- object[[1]]$formula
attributes(form) <- NULL
print(form)
for (effect in names(object)){
print(summary(object[[effect]], ...))
}
invisible(NULL)
}
print.effpoly <- function(x, type=c("probability", "logits"), ...){
type <- match.arg(type)
x.frame <-as.data.frame(x)
n.predictors <- length(names(x$x))
predictors <- names(x.frame)[1:n.predictors]
y.lev <- x$y.lev
ylevel.names <- make.names(paste("prob",y.lev))
colnames(x$prob) <- colnames(x$logit) <- ylevel.names
y.categories <- matrix(0, nrow=length(x.frame[,predictors[1]]), ncol=length(y.lev))
for (i in 1:length(y.lev)){
level <- which(colnames(x$prob)[i] == ylevel.names)
y.categories[,i] <- rep(y.lev[level], length(y.categories[,i]))
}
y.categories <- as.vector(y.categories)
y.categories <- factor(y.categories)
for (i in 1:length(y.lev)){
cat(paste("\n", gsub(":", "*", x$term), " effect (", type,") for ", y.lev[i], "\n", sep=""))
table <- array(if (type == "probability") {x$prob[y.categories==y.lev[i]]}
else {x$logit[y.categories==y.lev[i]]},
dim=sapply(x$variables, function(x) length(x$levels)),
dimnames=lapply(x$variables, function(x) x$levels))
print(table, ...)
}
if (x$discrepancy > 0.1) cat(paste("\nWarning: There is an average discrepancy of",
round(x$discrepancy, 2),
"percent \n in the 'safe' predictions for effect", x$term, '\n'))
invisible(x)
}
summary.effpoly <- function(object, type=c("probability", "logits"), ...){
type <- match.arg(type)
x.frame <-as.data.frame(object)
n.predictors <- length(names(object$x))
predictors <- names(x.frame)[1:n.predictors]
y.lev <- object$y.lev
ylevel.names <- make.names(paste("prob",y.lev))
colnames(object$prob) <- colnames(object$logit) <-
colnames(object$lower.logit) <- colnames(object$upper.logit) <-
colnames(object$lower.prob) <- colnames(object$upper.prob)<- ylevel.names
y.categories <-matrix(0, nrow=length(x.frame[,predictors[1]]), ncol=length(y.lev))
for (i in 1:length(y.lev)){
level <- which(colnames(object$prob)[i] == ylevel.names)
y.categories[,i] <- rep(y.lev[level], length(y.categories[,i]))
}
y.categories <- as.vector(y.categories)
y.categories <- factor(y.categories)
for (i in 1:length(y.lev)){
cat(paste("\n", gsub(":", "*", object$term), " effect (" , type, ") for ", y.lev[i], "\n", sep=""))
table <- array(if (type == "probability") {object$prob[y.categories==y.lev[i]]}
else {object$logit[y.categories==y.lev[i]]},
dim=sapply(object$variables, function(x) length(x$levels)),
dimnames=lapply(object$variables, function(x) x$levels))
print(table, ...)
}
if (is.null(object$confidence.level)) return(invisible(NULL))
for (i in 1:length(y.lev)){
cat(paste("\n", 'Lower', object$confidence.level*100, 'Percent Confidence Limits for'
, y.lev[i],'\n'))
table <- if (type == "probability") object$lower.prob else object$lower.logit
table <- array(table[y.categories==y.lev[i]],
dim=sapply(object$variables, function(x) length(x$levels)),
dimnames=lapply(object$variables, function(x) x$levels))
print(table, ...)
}
for (i in 1:length(y.lev)){
cat(paste("\n", 'Upper', object$confidence.level*100, 'Percent Confidence Limits for'
, y.lev[i],'\n'))
table <- if (type == "probability") object$upper.prob else object$upper.logit
table <- array(table[y.categories==y.lev[i]],
dim=sapply(object$variables, function(x) length(x$levels)),
dimnames=lapply(object$variables, function(x) x$levels))
print(table, ...)
}
if (object$discrepancy > 0.1) cat(paste("\nWarning: There is an average discrepancy of",
round(object$discrepancy, 2),
"percent \n in the 'safe' predictions for effect", object$term, '\n'))
invisible(NULL)
}
print.efflatent <- function(x, ...){
cat(paste("\n", gsub(":", "*", x$term), 'effect\n'))
table <- array(x$fit,
dim=sapply(x$variables, function(x) length(x$levels)),
dimnames=lapply(x$variables, function(x) x$levels))
print(table, ...)
cat("\nThresholds:\n")
print(x$thresholds, ...)
if (x$discrepancy > 0.1) cat(paste("\nWarning: There is an average discrepancy of",
round(x$discrepancy, 3),
"percent \n in the 'safe' predictions for effect", x$term, '\n'))
invisible(x)
}
summary.efflatent <- function(object, ...){
result <- list()
result$header <- paste("\n", gsub(":", "*", object$term), 'effect\n')
result$effect <- array(object$fit,
dim=sapply(object$variables, function(x) length(x$levels)),
dimnames=lapply(object$variables, function(x) x$levels))
if (!is.null(object$se)){
result$lower.header <- paste('\n Lower', round(100*object$confidence.level, 2),
'Percent Confidence Limits\n')
result$lower <- array(object$lower,
dim=sapply(object$variables, function(x) length(x$levels)),
dimnames=lapply(object$variables, function(x) x$levels))
result$upper.header <- paste('\n Upper', round(100*object$confidence.level, 2),
'Percent Confidence Limits\n')
result$upper <- array(object$upper,
dim=sapply(object$variables, function(x) length(x$levels)),
dimnames=lapply(object$variables, function(x) x$levels))
}
result$thresholds <- object$thresholds
if (object$discrepancy > 0.1) result$warning <- paste("\nWarning: There is an average discrepancy of",
round(object$discrepancy, 3),
"percent \n in the 'safe' predictions for effect", object$term, '\n')
class(result) <- "summary.eff"
result
}
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