collinear: Filter to reduce collinearity in predictors

In nestedcv: Nested Cross-Validation with 'glmnet' and 'caret'

Filter to reduce collinearity in predictors

Description

This function identifies predictors with r^2 above a given cut-off and produces an index of predictors to be removed. The function takes a matrix or data.frame of predictors, and the columns need to be ordered in terms of importance - first column of any pair that are correlated is retained and subsequent columns which correlate above the cut-off are flagged for removal.

Usage

collinear(x, rsq_cutoff = 0.9, rsq_method = "pearson", verbose = FALSE)

Arguments

x	A matrix or data.frame of values. The order of columns is used to determine which columns to retain, so the columns in x should be sorted with the most important columns first.
rsq_cutoff	Value of cut-off for r-squared
rsq_method	character string indicating which correlation coefficient is to be computed. One of "pearson" (default), "kendall", or "spearman". See cor().
verbose	Boolean whether to print details

Value

Integer vector of the indices of columns in x to remove due to collinearity

nestedcv documentation built on Oct. 26, 2023, 5:08 p.m.