PPclassify2

0th

Percentile

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

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

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

Value

predict.class predicted class

predict.error 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.

Aliases
  • PPclassify2
Examples
# NOT RUN {
#crab data set

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

PPclassify2(Tree.crab)

# }
Documentation reproduced from package PPforest, version 0.1.1, License: GPL (>= 2)

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