PP.classify: Predict class for the test set and calculate prediction error
In EK-Lee/PPtree: Projection pursuit classification tree

Description Usage Arguments Value References Examples

After finding tree structure, predict class for the test set and calculate prediction error.

1	PP.classify(test.data, true.class, Tree.result, Rule, ...)

`test.data`	the test dataset
`true.class`	true class of test dataset if available
`Tree.result`	the result of PP.Tree
`Rule`	split rule 1:mean of two group means, 2:weighted mean, 3: mean of max(left group) and min(right group), 4: weighted mean of max(left group) and min(right group)

predict.class predicted class

predict.error prediction error

Lee, YD, Cook, D., Park JW, and Lee, EK(2013) PPtree: Projection pursuit classification tree, Electronic Journal of Statistics, 7:1369-1386.

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 <- PP.Tree("LDA",iris[train,5],iris[train,1:4])
PP.classify(iris[test,1:4],iris[test,5],Tree.result,1)