predict PPtree — PPclassify

Predict class for the test set with the fitted projection pursuit classification tree and calculate prediction error.

Usage

PPclassify_MOD(Tree.result,test.data,true.class=NULL,...)

Arguments

Tree.result: PPtreeclass object
test.data: the test dataset
true.class: true class of test dataset if available
...: arguments to be passed to methods

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_MOD(formula = Species~.,data = iris[train,],PPmethod = "LDA")
PPclassify_MOD(Tree.result,test.data = iris[test,1:4], true.class = iris[test,5])
#> $predict.error
#> [1] 0
#> 
#> $predict.class
#>  [1] "setosa"     "setosa"     "setosa"     "setosa"     "versicolor"
#>  [6] "versicolor" "versicolor" "versicolor" "versicolor" "versicolor"
#> [11] "virginica"  "virginica"  "virginica"  "virginica"  "virginica" 
#>