Description Usage Arguments Value References Examples

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

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

`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 |

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.

1 2 3 4 5 6 | ```
#crab data set
Tree.crab <- PPtree_split('Type~.', data = crab, PPmethod = 'LDA', size.p = 0.5)
Tree.crab
PPclassify2(Tree.crab)
``` |

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