PPforest (version 0.1.3)

PPclassify2: Predict class for the test set and calculate prediction error after finding the PPtree structure, .

Description

Predict class for the test set and calculate prediction error after finding the PPtree structure, .

Usage

PPclassify2( Tree.result, test.data = NULL, Rule = 1, true.class = NULL)

Value

predict.class predicted class

predict.error prediction error

Arguments

Tree.result

the result of PP.Tree

test.data

the test dataset

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)

true.class

true class of test dataset if available

References

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

Examples

#crab data set

Tree.crab <- PPtree_split('Type~.', data = crab, PPmethod = 'LDA', size.p = 0.5)
Tree.crab

PPclassify2(Tree.crab)