baggtree: For each bootstrap sample grow a projection pursuit tree...

In natydasilva/PPforest2: Projection Pursuit Classification Forest

For each bootstrap sample grow a projection pursuit tree (PPtree object).

Description

For each bootstrap sample grow a projection pursuit tree (PPtree object).

Usage

baggtree(
  data,
  class,
  m = 500,
  PPmethod = "LDA",
  lambda = 0.1,
  size.p = 1,
  parallel = FALSE,
  cores = 2
)

Arguments

data	Data frame with the complete data set.
class	A character with the name of the class variable.
m	is the number of bootstrap replicates, this corresponds with the number of trees to grow. To ensure that each observation is predicted a few times we have to select this number no too small. m = 500 is by default.
PPmethod	is the projection pursuit index to be optimized, options LDA or PDA, by default it is LDA.
lambda	a parameter for PDA index
size.p	proportion of random sample variables in each split if size.p= 1 it is bagging and if size.p<1 it is a forest.
parallel	logical condition, if it is TRUE then parallelize the function
cores	number of cores used in the parallelization

Value

data frame with trees_pp output for all the bootstraps samples.

Examples

#crab data set
crab.trees <- baggtree(data = crab, class = 'Type',
m =  200, PPmethod = 'LDA', lambda = .1, size.p = 1 , parallel = TRUE, cores = 2)
str(crab.trees, max.level = 1)

natydasilva/PPforest2 documentation built on July 3, 2025, 8:58 p.m.