corr_rm: Filter Data Frame by Correlation Values

In meantrix/corrp: Compute Correlations Type Analysis in Parallel

Filter Data Frame by Correlation Values

Description

Remove highly correlated variables from a data frame using the correlation functions' outputs and the findCorrelation function from the caret package.

Usage

corr_rm(df, c, ...)

## S3 method for class 'clist'
corr_rm(
  df,
  c,
  col = c("infer.value", "stat.value"),
  isig = TRUE,
  cutoff = 0.75,
  ...
)

## S3 method for class 'list'
corr_rm(
  df,
  c,
  col = c("infer.value", "stat.value"),
  isig = TRUE,
  cutoff = 0.75,
  ...
)

## S3 method for class 'cmatrix'
corr_rm(df, c, cutoff = 0.75, ...)

## S3 method for class 'matrix'
corr_rm(df, c, cutoff = 0.75, ...)

Arguments

df	[data.frame(1)] The input data frame.
c	[clist(1) | cmatrix] A correlation list output from the corrp function (with class clist), or a correlation matrix output from the corr_matrix function (with class cmatrix).
...	Additional arguments.
col	[character(1)] The column to be used in the correlation matrix.
isig	[logical(1)] Whether values that are not statistically significant should be represented by NA or FALSE in the correlation matrix.
cutoff	[numeric(1)] A numeric value for the pairwise absolute correlation cutoff. The default value is 0.75.

Author(s)

Igor D.S. Siciliani, Paulo H. dos Santos

Examples

iris_clist <- corrp(iris)
iris_cmatrix <- corr_matrix(iris_clist)
corr_rm(df = iris, c = iris_clist, cutoff = 0.75, col = "infer.value", isig = FALSE)
corr_rm(df = iris, c = iris_cmatrix, cutoff = 0.75, col = "infer.value", isig = FALSE)

meantrix/corrp documentation built on April 17, 2025, 7:22 p.m.