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# Modified 25 Nov 2009 for point marking
# 20 Jan 2010: changed line types. J. Fox
# 15 August 2010: fixed colors of points
# 18 January 2011; added robust M estimation
# 2017-02-13: consolidated id argument. John
# 2017-02-16: replace rlm() calls with MASS::rlm()
# 2017-11-30: substitute carPalette() for palette(). J. Fox
# 2020-02-17: replaced match.fun by matchFun in utility-functions.R
invTranPlot <- function(x,...) UseMethod("invTranPlot")
invTranPlot.formula <- function(x, data, subset, na.action, id=FALSE, ...) {
mf <- call <- match.call()
m <- match(c("x", "data", "subset", "na.action"),
names(mf), 0L)
mf <- mf[c(1L,m)]
mf$drop.unused.levels <- TRUE
mf[[1L]] <- as.name("model.frame")
names(mf)[which(names(mf)=="x")] <- "formula"
mf <- eval(mf, parent.frame())
if(dim(mf)[2] != 2) stop("Formula must be of the form y ~ x")
id <- applyDefaults(id, defaults=list(method="x", n=2, cex=1, col=carPalette()[1], location="lr"), type="id")
if (isFALSE(id)){
id.n <- 0
id.method <- "none"
labels <- id.cex <- id.col <- id.location <- NULL
}
else{
labels <- id$labels
if (is.null(labels)) labels <- rownames(mf)
id.method <- id$method
id.n <- if ("identify" %in% id.method) Inf else id$n
id.cex <- id$cex
id.col <- id$col
id.location <- id$location
}
vx <- mf[,2]
vy <- mf[,1]
if( is.null(call$xlab) & is.null(call$ylab))
invTranPlot(vx, vy, xlab=colnames(mf)[2], ylab=colnames(mf)[1],
id=list(n=id.n, method=id.method, labels=labels, cex=id.cex, col=id.col, location=id.location), ...) else
if(is.null(call$xlab) & !is.null(call$ylab))
invTranPlot(vx, vy, xlab=colnames(mf)[2],
id=list(n=id.n, method=id.method, labels=labels, cex=id.cex, col=id.col, location=id.location), ...) else
if(!is.null(call$xlab) & is.null(call$ylab))
invTranPlot(vx ,vy ,ylab=colnames(mf)[1],
id=list(n=id.n, method=id.method, labels=labels, cex=id.cex, col=id.col, location=id.location), ...) else
invTranPlot(vx, vy, id=list(n=id.n, method=id.method, labels=labels, cex=id.cex, col=id.col, location=id.location), ...)
}
invTranPlot.default<- function(x, y, lambda=c(-1, 0, 1), robust=FALSE,
lty.lines=rep(c("solid", "dashed", "dotdash", "longdash", "twodash"),
length=1 + length(lambda)), lwd.lines=2,
col=carPalette()[1], col.lines=carPalette(),
xlab=deparse(substitute(x)), ylab=deparse(substitute(y)),
family="bcPower", optimal=TRUE, key="auto", id=FALSE,
grid=TRUE, ...){
id <- applyDefaults(id, defaults=list(method="x", n=2, cex=1, col=carPalette()[1], location="lr"), type="id")
if (isFALSE(id)){
id.n <- 0
id.method <- "none"
labels <- id.cex <- id.col <- id.location <- NULL
}
else{
labels <- id$labels
if (is.null(labels)) labels <- seq(along=x)
id.method <- id$method
id.n <- if ("identify" %in% id.method) Inf else id$n
id.cex <- id$cex
id.col <- id$col
id.location <- id$location
}
if (is.factor(x)) stop("Predictor variable may not be a factor")
if (is.factor(y)) stop("Response variable may not be a factor")
if (optimal){
opt <- invTranEstimate(x, y, family=family, confidence=FALSE, robust=robust)
lam <- c(opt$lambda, lambda)} else lam <- lambda
fam <- matchFun(family)
plot(x, y, xlab=xlab, ylab=ylab, type="n", col=col, ...)
if(grid){
grid(lty=1, equilogs=FALSE)
box()}
points(x, y, col=col, ...)
rss <- NULL
new <- seq(min(x, na.rm=TRUE), max(x,na.rm=TRUE), length=100)
for (j in 1:length(lam)){
m1 <- if(robust) MASS::rlm(y ~ fam(x, lam[j])) else lm(y~fam(x, lam[j]))
rss <- c(rss, sum(residuals(m1)^2))
lines(new,predict(m1, data.frame(x=new)), lty=lty.lines[j],
col=col.lines[j], lwd=lwd.lines)}
showLabels(x, y, labels=labels,
method=id.method, n=id.n, cex=id.cex,
col=id.col, location = id.location)
if (!is.null(key)) {
loc <- key
if(length(lam) <= 4) {
lims <- par("usr")[c(1,4)]
llam <- expression(paste(hat(lambda), ":"))
text(lims[1],lims[2], llam, xpd=TRUE, pos=3)
outerLegend(
as.character(round(lam,2)),
lwd=lwd.lines, lty=lty.lines,
col=col.lines,
bty="n",
cex=0.85, fill=col.lines,
border=col.lines, horiz=TRUE, adjust=FALSE)}
else {
legend(ifelse(cor(x, y)>0,"bottomright","topright"),
legend = c(expression(hat(lambda)),as.character(round(lam,2))),
lwd=lwd.lines, lty=c("blank", lty.lines), col=c("#00000000",col.lines),
inset=0.02, cex=0.75, fill=c("#00000000",col.lines),
border=c("#00000000",col.lines))
}}
data.frame(lambda=lam, RSS=rss)
}
invTranEstimate <- function(x, y, family="bcPower", confidence=0.95,
robust=FALSE){
if (is.factor(x)) stop("Predictor variable may not be a factor")
if (is.factor(y)) stop("Response variable may not be a factor")
if (robust) confidence <- FALSE
fam <- matchFun(family)
f <- if(robust==FALSE)
function(lambda,x,y,family){deviance(lm(y~fam(x,lambda)))} else
function(lambda,x,y,family){sum(residuals(MASS::rlm(y ~ fam(x,lambda)))^2)}
lhat <- optimize(f = function(lambda) f(lambda, x, y, family), interval=c(-10,10))
if (confidence==FALSE){ return(list(lambda=lhat$minimum)) } else {
g <- lm(y~fam(x,lhat$minimum))
n = length(residuals(g))
dev0 <- -n*log(deviance(g))
cutoff <- qchisq(confidence,1)/2
f1 <- function(lam) abs(dev0 + n*log(deviance(lm(y~fam(x,lam)))) -cutoff)
lowlim <- optimize(f1, interval=c(-10,lhat$minimum))
hilim <- optimize(f1, interval=c(lhat$minimum,10))
return(list(lambda=lhat$minimum,lowerCI=lowlim$minimum,upperCI=hilim$minimum))}
}
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