R version 2.15.0 (2012-03-30)
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> #
> # Test weights in a regression problem
> #
> library(rpart)
> mystate <- data.frame(state.x77, region=factor(state.region))
> names(mystate) <- c("population","income" , "illiteracy","life" ,
+        "murder", "hs.grad", "frost",     "area",      "region")
> 
> xgrp <- rep(1:10,5)
> fit4 <- rpart(income ~ population + region + illiteracy +life + murder +
+                         hs.grad + frost , mystate,
+                    control=rpart.control(minsplit=10, xval=xgrp))
> wts <- rep(3, nrow(mystate))
> fit4b <-  rpart(income ~ population + region + illiteracy +life + murder +
+                         hs.grad + frost , mystate,
+                    control=rpart.control(minsplit=10, xval=xgrp), weights=wts)
> fit4b$frame$wt   <- fit4b$frame$wt/3
> fit4b$frame$dev  <- fit4b$frame$dev/3
> fit4b$cptable[,5] <- fit4b$cptable[,5] * sqrt(3)
> temp <- c('frame', 'where', 'splits', 'csplit', 'cptable')
> all.equal(fit4[temp], fit4b[temp])  
[1] TRUE
> 
> 
> # Next is a very simple case, but worth keeping
> dummy <- data.frame(y=1:10, x1=c(10:4, 1:3), x2=c(1,3,5,7,9,2,4,6,8,0))
> 
> xx1 <- rpart(y ~ x1 + x2, dummy, minsplit=4, xval=0)
> xx2 <- rpart(y ~ x1 + x2, dummy, weights=rep(2,10), minsplit=4, xval=0)
> 
> all.equal(xx1$frame$dev, c(82.5, 10, 2, .5, 10, .5, 2))
[1] TRUE
> all.equal(xx2$frame$dev, c(82.5, 10, 2, .5, 10, .5, 2)*2)
[1] TRUE
> 
> # Now for a set of non-equal weights
> #  We need to set maxcompete=3 because there just happens to be, in one
> #  of the lower nodes, an exact tie between variables "life" and "murder".
> #  Round off error causes fit5 to choose one and fit5b the other.
> # Later -- cut it back to maxdepth=3 for the same reason (a tie).
> #
> nn <- nrow(mystate)
> wts <- rep(1:5, length=nn)
> temp <- rep(1:nn, wts)             #row replicates
> xgrp <- rep(1:10, length=nn)
> xgrp2<- rep(xgrp, wts)
> tempc <- rpart.control(minsplit=2, xval=xgrp2, maxsurrogate=0,
+ 		       maxcompete=3, maxdepth=3)
> #  Direct: replicate rows in the data set, and use unweighted
> fit5 <-  rpart(income ~ population + region + illiteracy +life + murder +
+                         hs.grad + frost , data=mystate[temp,], control=tempc)
> #  Weighted
> tempc <- rpart.control(minsplit=2, xval=xgrp, maxsurrogate=0,
+ 		       maxcompete=3, maxdepth=3)
> fit5b <-  rpart(income ~ population + region + illiteracy +life + murder +
+                         hs.grad + frost , data=mystate, control=tempc,
+                         weights=wts)
> all.equal(fit5$frame[-2],  fit5b$frame[-2])  # the "n" component won't match
[1] TRUE
> all.equal(fit5$cptable,    fit5b$cptable)
[1] TRUE
> all.equal(fit5$splits[,-1],fit5b$splits[,-1]) 
[1] TRUE
> all.equal(fit5$csplit,    fit5b$csplit)
[1] TRUE
> 
> proc.time()
   user  system elapsed 
  0.283   0.064   0.394