reduce_high_cor_filter: Filtering highly correlated variables with reduce method

In creditmodel: Toolkit for Credit Modeling, Analysis and Visualization

Description

reduce_high_cor_filter is function for filtering highly correlated variables with reduce method.

Usage

reduce_high_cor_filter(
  dat,
  x_list = NULL,
  size = ncol(dat)/10,
  p = 0.95,
  com_list = NULL,
  ex_cols = NULL,
  cor_class = TRUE,
  parallel = FALSE
)

Arguments

dat	A data.frame with independent variables.
x_list	Names of independent variables.
size	Size of vairable group.
p	Threshold of correlation between features. Default is 0.7.
com_list	A data.frame with important values of each variable. eg : IV_list
ex_cols	A list of excluded variables. Regular expressions can also be used to match variable names. Default is NULL.
cor_class	Culculate catagery variables's correlation matrix. Default is FALSE.
parallel	Logical, parallel computing. Default is FALSE.

creditmodel documentation built on Jan. 7, 2022, 5:06 p.m.