PPtreeViz (version 2.0.4)

PPclassify: 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

Value

predict.class predicted class

predict.error number of the prediction errors

Details

Predict class for the test set with the fitted projection pursuit classification tree and calculate prediction error.

References

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

Examples

# NOT RUN {
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])
# }