Hi dear R-help memebers,
When building a CART model (specifically classification tree) using rpart,
it is sometimes obvious that there are variables (X's) that are meaningful
for predicting some of the outcome (y) variables - while other predictors
are relevant for other outcome variables (y's only).
*How can it be estimated, which explanatory variable is "used" for which of
the predicted value in the outcome variable?*
Here is an example code in which x2 is the only important variable for
predicting "b" (one of the y outcomes). There is no predicting variable for
"c", and x1 is a predictor for "a", assuming that x2 permits it.
How can this situation be shown using the an rpart fitted model?
N <- 200
set.seed(5123)
x1 <- runif(N)
x2 <- runif(N)
x3 <- runif(N)
y <- sample(letters[1:3], N, T)
y[x1 <.5] <- "a"
y[x2 <.1] <- "b"
fit <- rpart(y ~ x1+x2)
fit2 <- prune(fit, cp= 0.07)
plot(fit2)
text(fit2, use.n=TRUE)
Thanks,
Tal
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