Thanks.  Very helpful.

You can use the information from the splits in the first tree, to define a 
new grouping variable, which will simplify the plot:
suvar <- sort(unique(test_set$list_var))
test_set$var_grp <- as.factor(testtree$csplit[match(test_set$list_var, 
suvar)])
testtree2 <- rpart ( list_val ~ var_grp, data = test_set ) 
rpart.plot(testtree2, type=3) 

Not to other readers, you will need to load these packages, before running 
the code:
library(rpart)
library(rpart.plot)

Jean


MarkBeauchene <MarkBeauchene@hotmail.com> wrote on 07/09/2012 03:42:32 PM:
> Here is some sample code.  It generates a class (list_var) that is used 
in
> rpart.  list_val is the dependant variable.
> 
> The plot shows all the values of the class, which is a mess and makes 
the
> plot unuseable.  I'd like to either suppress the list entirely or 
replace it
> with something like "Group 1", "Group 2", etc.
> 
> list_var <- rep(NA,2000)
> list_val <- rep(NA,2000)
> for (i in 1:1000) {
> list_var[i] <- paste("A",i%/%25,sep='')
> list_val[i] <- runif(1,0,1) }
> test_set <- data.frame(list_var, list_val )
> 
> 
> 
> 
> testtree <- rpart ( list_val ~ list_var, data = test_set )
> rpart.plot(testtree, type=3)

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