35 lines
1.2 KiB
R
35 lines
1.2 KiB
R
#
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# Test out the "return.all" argument of xpred
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# The data set has the virtue of continuous, categorical, and missings
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#
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library(rpart)
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require(survival)
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set.seed(10)
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fit1 <- rpart(Surv(pgtime, pgstat) ~ age + eet + g2+grade+gleason +ploidy,
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stagec, method='poisson')
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xgrp <- rep(1:3, length=nrow(stagec)) # explicitly set the xval groups
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xfit1 <- xpred.rpart(fit1, xval=xgrp, return.all=T)
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xfit2 <- array(0, dim=dim(xfit1))
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cplist <- as.numeric(dimnames(xfit1)[[2]])
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for (i in 1:3) {
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tfit <- rpart(Surv(pgtime, pgstat) ~ age + eet + g2+grade+gleason +ploidy,
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stagec, method='poisson', subset=(xgrp !=i))
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# xvals are actually done on the absolute risk (node's risk /n), not on
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# the rescaled risk ((node risk)/ (top node risk)) which is the basis
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# for the printed CP. To get the right answer we need to rescale.
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cp2 <- cplist * (fit1$frame$dev[1] / fit1$frame$n[1]) /
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(tfit$frame$dev[1] / tfit$frame$n[1])
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for (j in 1:length(cp2)) {
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tfit2 <- prune(tfit, cp=cp2[j])
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temp <- predict(tfit2, newdata=stagec[xgrp==i,], type='matrix')
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xfit2[xgrp==i, j,] <- temp
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}
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}
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all.equal(xfit1, xfit2, check.attributes=FALSE)
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