deleteZeroOrNearZeroVariance.mb {Coxmos}

deleteZeroOrNearZeroVariance.mb

Description

Provides a robust mechanism to filter out variables from a dataset that exhibit zero or near-zero variance, thereby enhancing the quality and interpretability of subsequent statistical analyses.

Usage

deleteZeroOrNearZeroVariance.mb(
  X,
  remove_near_zero_variance = FALSE,
  remove_zero_variance = TRUE,
  toKeep.zv = NULL,
  freqCut = 95/5
)

Arguments

X

List of numeric matrices or data.frame. Explanatory variables. Qualitative variables must be transform into binary variables.

remove_near_zero_variance

Logical. If remove_near_zero_variance = TRUE, near zero variance variables will be removed (default: TRUE).

remove_zero_variance

Logical. If remove_zero_variance = TRUE, zero variance variables will be removed (default: TRUE).

toKeep.zv

Character vector. Name of variables in X to not be deleted by (near) zero variance filtering (default: NULL).

freqCut

Numeric. Cutoff for the ratio of the most common value to the second most common value (default: 95/5).

Details

The deleteZeroOrNearZeroVariance function is an indispensable tool in the preprocessing phase of statistical modeling. In many datasets, especially high-dimensional ones, certain variables might exhibit zero or near-zero variance. Such variables can be problematic as they offer limited information variance and can potentially distort the results of statistical models, leading to issues like overfitting. By leveraging the caret::nearZeroVar() function, this tool offers a rigorous method to identify and exclude these variables. Users are afforded flexibility in their choices, with options to remove only zero variance variables, near-zero variance variables, or both. The function also provides the capability to set a frequency cutoff, freqCut, which determines the threshold for near-zero variance based on the ratio of the most frequent value to the second most frequent value. For scenarios where certain variables are deemed essential and should not be removed regardless of their variance, the toKeep.zv parameter allows users to specify a list of such variables.

Value

A list of two objects. X: A list with as many blocks as X input, but with the variables filtered. variablesDeleted: A list with as many blocks as X input, with the name of the variables that have been removed.

Author(s)

Pedro Salguero Garcia. Maintainer: [e-mail]

Examples

data("X_multiomic")
X <- X_multiomic
filter <- deleteZeroOrNearZeroVariance.mb(X, remove_near_zero_variance = TRUE)