PPclassify: predict PPtree

In EK-Lee/PPtreeViz: Projection Pursuit Classification Tree Visualization

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])

EK-Lee/PPtreeViz documentation built on May 6, 2019, 3:09 p.m.