Obtain predicted class for new data from baggtree function or PPforest

Usage

trees_pred(object, xnew, parallel = FALSE, cores = 2, rule = 1)

Arguments

object: Projection pursuit classification forest structure from PPforest or baggtree
xnew: data frame with explicative variables used to get new predicted values.
parallel: logical condition, if it is TRUE then parallelize the function
cores: number of cores used in the parallelization
rule: split rule 1: mean of two group means 2: weighted mean of two group means - weight with group size 3: weighted mean of two group means - weight with group sd 4: weighted mean of two group means - weight with group se 5: mean of two group medians 6: weighted mean of two group medians - weight with group size 7: weighted mean of two group median - weight with group IQR 8: weighted mean of two group median - weight with group IQR and size

Value

predicted values from PPforest or baggtree

Examples

if (FALSE) {
crab.trees <- baggtree(data = crab, class = 'Type', 
m =  200, PPmethod = 'LDA', lambda = .1, size.p = 0.4 )
 
pr <- trees_pred(  crab.trees,xnew = crab[, -1], parallel= FALSE, cores = 2)

pprf.crab <- PPforest(data = crab, class = 'Type',
 std = FALSE, size.tr = 2/3, m = 100, size.p = .4, PPmethod = 'LDA', parallel = TRUE )
 
trees_pred(pprf.crab, xnew = pprf.crab$test ,parallel = TRUE)
}