| PPclassify {PPtreeViz} | R Documentation |
predict PPtree
Description
predict projection pursuit classification tree
Usage
PPclassify(Tree.result,test.data,Rule,true.class=NULL,...)
Arguments
Tree.result |
PPtreeclass object |
test.data |
the test dataset |
Rule |
split rule 1: mean of two group means 2: weighted mean of two group means - weight with group size 3: weighted mean of two group means - weight with group sd 4: weighted mean of two group means - weight with group se 5: mean of two group medians 6: weighted mean of two group medians - weight with group size 7: weighted mean of two group median - weight with group IQR 8: weighted mean of two group median - weight with group IQR and size |
true.class |
true class of test dataset if available |
... |
arguments to be passed to methods |
Details
Predict class for the test set with the fitted projection pursuit classification tree and calculate prediction error.
Value
predict.class predicted class
predict.error number of the prediction errors
References
Lee, YD, Cook, D., Park JW, and Lee, EK(2013) PPtree: Projection Pursuit Classification Tree, Electronic Journal of Statistics, 7:1369-1386.
Examples
data(iris)
n <- nrow(iris)
tot <- c(1:n)
n.train <- round(n*0.9)
train <- sample(tot,n.train)
test <- tot[-train]
Tree.result <- PPTreeclass(Species~.,data=iris[train,],"LDA")
PPclassify(Tree.result,iris[test,1:4],1,iris[test,5])