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calibrate.default <- function(fit, predy,
method=c("boot","crossvalidation",".632","randomization"),
B=40, bw=FALSE, rule=c("aic","p"),
type=c("residual","individual"),
sls=.05, pr=FALSE, kint, smoother="lowess", ...) {
call <- match.call()
method <- match.arg(method)
rule <- match.arg(rule)
type <- match.arg(type)
ns <- num.intercepts(fit)
if(missing(kint)) kint <- floor((ns+1)/2)
clas <- attr(fit,"class")
model <- if(any(clas=="lrm"))"lr" else if(any(clas=="ols"))"ol" else
stop("fit must be from lrm or ols")
lev.name <- NULL
yvar.name <- as.character(formula(fit))[2]
y <- fit$y
n <- length(y)
if(length(y)==0) stop("fit did not use x=T,y=T")
if(model=="lr") {
y <- factor(y); lev.name <- levels(y)[kint+1]; fit$y <- as.integer(y)-1
## was category(y) y-1 11Apr02
}
predicted <- if(model=="lr")
1/(1+exp(-(fit$linear.predictors-fit$coef[1]+fit$coef[kint]))) else
fit$linear.predictors
if(missing(predy)) {
if(n<11) stop("must have n>10 if do not specify predy")
p <- sort(predicted)
predy <- seq(p[5],p[n-4],length=50)
p <- NULL
}
penalty.matrix <- fit$penalty.matrix
cal.error <- function(x, y, iter, smoother, predy, kint, model, ...) {
if(model=="lr") {
x <- 1/(1+exp(-x))
y <- y>=kint
}
smo <- if(is.function(smoother)) smoother(x,y) else lowess(x,y,iter=0)
cal <- if(.R.) approx(smo, xout=predy, ties=function(x)x[1])$y else
approx(smo, xout=predy)$y
## 11Apr01 .R. lowess has duplicates
# if(iter==0) assign(".orig.cal",cal,where=1) 17Apr01
if(iter==0) storeTemp(cal,".orig.cal")
cal-predy
}
fitit <- function(x, y, model, penalty.matrix=NULL, xcol=NULL, ...) {
if(length(penalty.matrix) && length(xcol)) {
if(model=='ol') xcol <- xcol[-1]-1 # take off intercept position
penalty.matrix <- penalty.matrix[xcol,xcol,drop=FALSE]
}
switch(model,
lr=lrm.fit(x, y, penalty.matrix=penalty.matrix,tol=1e-13),
ol=c(if(length(penalty.matrix)==0) lm.fit.qr.bare(x, y, intercept=FALSE) else
lm.pfit(x, y,
penalty.matrix=penalty.matrix),fail=FALSE))
## Was lm.fit.qr 14Sep00
}
z <- predab.resample(fit, method=method, fit=fitit, measure=cal.error,
pr=pr, B=B, bw=bw, rule=rule, type=type, sls=sls,
non.slopes.in.x=model=="ol",
smoother=smoother, predy=predy, model=model, kint=kint,
penalty.matrix=penalty.matrix, ...)
z <- cbind(predy, calibrated.orig=.orig.cal,
calibrated.corrected=.orig.cal-z[,"optimism"],
z)
structure(z, class="calibrate.default", call=call, kint=kint, model=model,
lev.name=lev.name, yvar.name=yvar.name, n=n, freq=fit$freq,
non.slopes=ns, B=B, method=method,
predicted=as.single(predicted), smoother=smoother)
}
print.calibrate.default <- function(x, ...) {
at <- attributes(x)
cat("\nEstimates of Calibration Accuracy by ",at$method," (B=",at$B,")\n\n",
sep="")
dput(at$call)
if(at$model=="lr") {
lab <- paste("Pr{",at$yvar.name,sep="")
if(at$non.slopes==1) lab <- paste(lab,"=",at$lev.name,"}",sep="") else
lab <- paste(lab,">=",at$lev.name,"}",sep="")
} else lab <- at$yvar.name
cat("\nPrediction of",lab,"\n\n")
predicted <- at$predicted
if(length(predicted)) { ## for downward compatibility
s <- !is.na(x[,'predy'] + x[,'calibrated.corrected'])
err <- predicted - approx(x[s,'predy'],x[s,'calibrated.corrected'],
xout=predicted)$y
cat('\nn=',length(err), ' Mean absolute error=',
format(mean(abs(err),na.rm=TRUE)),' Mean squared error=',
format(mean(err^2,na.rm=TRUE)), '\n0.9 Quantile of absolute error=',
format(quantile(abs(err),.9,na.rm=TRUE)), '\n\n',sep='')
}
print.matrix(x)
invisible()
}
plot.calibrate.default <- function(x, xlab, ylab, xlim, ylim, legend=TRUE,
subtitles=TRUE, ...){
at <- attributes(x)
if(missing(ylab)) ylab <- if(at$model=="lr") "Actual Probability" else
paste("Observed",at$yvar.name)
if(missing(xlab)) {
if(at$model=="lr") {
xlab <- paste("Predicted Pr{",at$yvar.name,sep="")
if(at$non.slopes==1) {
xlab <- if(at$lev.name=="TRUE") paste(xlab,"}",sep="") else
paste(xlab,"=",at$lev.name,"}",sep="")
} else
xlab <- paste(xlab,">=",at$lev.name,"}",sep="")
} else xlab <- paste("Predicted",at$yvar.name)
}
p <- x[,"predy"]
p.app <- x[,"calibrated.orig"]
p.cal <- x[,"calibrated.corrected"]
if(missing(xlim) & missing(ylim)) xlim <- ylim <- range(c(p,p.app,p.cal),
na.rm=TRUE) else {
if(missing(xlim)) xlim <- range(p)
if(missing(ylim)) ylim <- range(c(p.app,p.cal,na.rm=TRUE))
}
plot(p, p.app, xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab, type="n", ...)
predicted <- at$predicted
if(length(predicted)) { ## for downward compatibility
s <- !is.na(p + p.cal)
err <- predicted - approx(p[s],p.cal[s],xout=predicted)$y
cat('\nn=',n <- length(err), ' Mean absolute error=',
format(mae <- mean(abs(err),na.rm=TRUE)),' Mean squared error=',
format(mean(err^2,na.rm=TRUE)), '\n0.9 Quantile of absolute error=',
format(quantile(abs(err),.9,na.rm=TRUE)), '\n\n',sep='')
if(subtitles) title(sub=paste('Mean absolute error=',format(mae),
' n=',n,sep=''), cex=.65, adj=1)
scat1d(predicted)
}
lines(p, p.app, lty=3)
lines(p, p.cal, lty=1)
abline(a=0,b=1,lty=2)
if(subtitles) title(sub=paste("B=",at$B,"repetitions,",at$method),adj=0)
if(!(is.logical(legend) && !legend)) {
if(is.logical(legend)) legend <- list(x=xlim[1]+.55*diff(xlim), #was .57
y=ylim[1]+.32*diff(ylim))
legend(legend, c("Apparent","Bias-corrected","Ideal"),
lty=c(3,1,2), bty="n")
}
invisible()
}
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