# PPclassify2

From PPforest v0.1.1
by Natalia Silva

##### 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.

##### 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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