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> #
> # Test out the "return.all" argument of xpred
> #  The data set has the virtue of continuous, categorical, and missings
> #
> library(rpart)
> require(survival)
Loading required package: survival
Loading required package: splines
> 
> fit1 <- rpart(Surv(pgtime, pgstat) ~ age + eet + g2+grade+gleason +ploidy,
+                 stagec,  method='poisson')
> 
> xgrp <- rep(1:3, length=nrow(stagec))  # explicitly set the xval groups
> 
> xfit1 <- xpred.rpart(fit1, xval=xgrp, return.all=T)
> xfit2 <- array(0, dim=dim(xfit1))
> cplist <- as.numeric(dimnames(xfit1)[[2]])
> 
> for (i in 1:3) {
+     tfit <- rpart(Surv(pgtime, pgstat) ~ age + eet + g2+grade+gleason +ploidy,
+                 stagec,  method='poisson', subset=(xgrp !=i))
+     # xvals are actually done on the absolute risk (node's risk /n), not on
+     #   the rescaled risk ((node risk)/ (top node risk)) which is the basis
+     #   for the printed CP.  To get the right answer we need to rescale.
+     cp2 <- cplist * (fit1$frame$dev[1] / fit1$frame$n[1]) / 
+                     (tfit$frame$dev[1] / tfit$frame$n[1])
+ 
+     for (j in 1:length(cp2)) {
+         tfit2 <- prune(tfit, cp=cp2[j])
+         temp <- predict(tfit2, newdata=stagec[xgrp==i,], type='matrix')
+         xfit2[xgrp==i, j,] <- temp
+         }
+     }
> 
> all.equal(xfit1, xfit2, check.attributes=FALSE)
[1] TRUE
> 
> proc.time()
   user  system elapsed 
  0.449   0.072   0.558