tsp.tree(X, response, control = tree.control(dim(X)[1], ...), method = "recursive.partition", split = c("deviance", "gini"), x = FALSE, y = TRUE, wts = TRUE, ...)tree.control.
"model.frame".
"" for a terminal node), n, the (weighted) number of cases reaching that node, dev the deviance of the node, yval, the fitted value at the node (the mean for regression trees, a majority class for classification trees) and split, a two-column matrix of the labels for the left and right splits at the node. Classification trees also have yprob, a matrix of fitted probabilities for each response level.model = TRUE, the model frame.x = TRUE, the model matrix.y = TRUE, the response.wts = TRUE, the weights.Breiman L., Friedman J. H., Olshen R. A., and Stone, C. J. (1984) Classification and Regression Trees. Wadsworth.
Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge. Chapter 7.
predict.tsp.tree
library(tree)
x=matrix(rnorm(100*20),100,20)
y=rbinom(100,1,0.5)
y=as.factor(y)
data=data.frame(y,x)
tr=tsp.tree(x,y)
predict(tr,data[1:10,])
plot(tr)
text(tr)
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