orsf_vs: Variable selection

In aorsf: Accelerated Oblique Random Forests

Variable selection

Description

Variable selection

Usage

orsf_vs(object, n_predictor_min = 3, verbose_progress = NULL)

Arguments

object	(ObliqueForest) a trained oblique random forest object (see orsf).
n_predictor_min	(integer) the minimum number of predictors allowed
verbose_progress	(logical) not implemented yet. Should progress be printed to the console?

Details

The difference between variables_included and predictors_included is referent coding. The variable would be the name of a factor variable in the training data, while the predictor would be the name of that same factor with the levels of the factor appended. For example, if the variable is diabetes with levels = c("no", "yes"), then the variable name is diabetes and the predictor name is diabetes_yes.

tree_seeds should be specified in object so that each successive run of orsf will be evaluated in the same out-of-bag samples as the initial run.

Value

a data.table with four columns:

• n_predictors: the number of predictors used

• stat_value: the out-of-bag statistic

• variables_included: the names of the variables included

• predictors_included: the names of the predictors included

• predictor_dropped: the predictor selected to be dropped

Examples

object <- orsf(formula = time + status ~ .,
               data = pbc_orsf,
               n_tree = 25,
               importance = 'anova')

orsf_vs(object, n_predictor_min = 15)

aorsf documentation built on June 22, 2024, 10:31 a.m.