VIMPS (version 1.0)

calc_vimps: calc_vimps

Description

Calculate the variable importance of the domains for a given dataset

Usage

calc_vimps(
  dat,
  dep_var,
  doms,
  calc_ko = TRUE,
  calc_dom = FALSE,
  num_folds = 10,
  num_kos = 100,
  model_all = normal_model,
  model_subset = one_tree_model,
  mtry = NULL,
  min.node.size = NULL,
  iterations = 500,
  ko_path = NULL,
  results_path = NULL,
  output_file_ko = NULL,
  output_file_dom = NULL
)

Value

List with 1) Threshold for binary class labeling 2) Model metrics using all variables 3) Model metrics using knock-off variables 4) Variable importance with knock-offs

Arguments

dat

A dataframe of data

dep_var

The dependent variable in the dat

doms

A dataframe of the variables in dat and the domain they belong to

calc_ko

True/False to calculate the knock_off importance

calc_dom

True/False to calculate the domain importance

num_folds

The number of folds to use while calculating the classification threshold for predictions

num_kos

The number of sets of knock off variables to create

model_all

The model to use in full ensemble mode in calculations

model_subset

The model to use sigularly for building ensembles from

mtry

The mtry value to use in the random forests

min.node.size

The min.node.size value to use in the random forests

iterations

Number of trees to build while calculating variable importance

ko_path

Where to store the knock off variable sets

results_path

Where to store the intermediary results for calculating variable importance

output_file_ko

Where to store the results of the knock off variable importance

output_file_dom

Where to store the results of the domain variable importance

Examples

calc_vimps(
  data.frame(
    X1=c(2,8,3,9,1,4,3,8,0,9,2,8,3,9,1,4,3,8,0,9),
    X2=c(7,2,5,0,9,1,8,8,3,9,7,2,5,0,9,1,8,8,3,9),
    Y=c(0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,1,1,1,1,1)),
 "Y",
 data.frame(domain=c('X1','X2'),
 variable=c('X1','X2')),
 num_folds=2,
 num_kos=1,
 iterations=50)