filter_high_cor {tidysdm}

Filter to retain only variables below a given correlation threshold

Description

This method finds a subset of variable such that all have a correlation below a certain cutoff. There are methods for terra::SpatRaster, data.frame, and to work directly on a correlation matrix that was previously estimated. For data.frame, only numeric variables will be considered. The algorithm is based on caret::findCorrelation, using the exact option. The absolute values of pair-wise correlations are considered. If two variables have a high correlation, the function looks at the mean absolute correlation of each variable and removes the variable with the largest mean absolute correlation.

Usage

filter_high_cor(x, cutoff = 0.7, verbose = FALSE, names = TRUE, to_keep = NULL)

## Default S3 method:
filter_high_cor(x, cutoff = 0.7, verbose = FALSE, names = TRUE, to_keep = NULL)

## S3 method for class 'SpatRaster'
filter_high_cor(x, cutoff = 0.7, verbose = FALSE, names = TRUE, to_keep = NULL)

## S3 method for class 'data.frame'
filter_high_cor(x, cutoff = 0.7, verbose = FALSE, names = TRUE, to_keep = NULL)

## S3 method for class 'matrix'
filter_high_cor(x, cutoff = 0.7, verbose = FALSE, names = TRUE, to_keep = NULL)

filter_high_cor_algorithm(x, cutoff = 0.7, verbose = FALSE)

Arguments

x

A terra::SpatRaster object, a data.frame (with only numeric variables), or a correlation matrix

cutoff

A numeric value for the pair-wise absolute correlation cutoff

verbose

A boolean for printing the details

names

a logical; should the column names be returned TRUE or the column index FALSE)?

to_keep

A vector of variable names that we want to force in the set (note that the function will return an error if the correlation among any of those variables is higher than the cutoff).

Details

There are several function in the package subselect that can also be used to accomplish the same goal but tend to retain more predictors.

Value

A vector of names of columns that are below the correlation threshold (when names = TRUE), otherwise a vector of indices. Note that the indices are only for numeric variables (i.e. if factors are present, the indices do not take them into account).