vs_mram {MRAM}R Documentation

Variable Selection via the Multivariate Regression Association Measure

Description

Perform variable selection via the multivariate regression association measure proposed in Shih and Chen (2025).

Usage

vs_mram(y_data, x_data)

Arguments

y_data

A n \times d matrix of responses.

x_data

A n \times p matrix of predictors.

Details

vs_mram is a forward and stepwise variable selection algorithm which utilizes the multivariate regression association measure proposed in Shih and Chen (2025). The Algorithm is modified from the feature ordering by conditional independence (FOCI) algorithm from Azadkia and Chatterjee (2021).

Value

The vector containing the indices of the selected predictors in the order they were chosen.

References

Azadkia and Chatterjee (2021) A simple measure of conditional dependence, Annals of Statistics, 46(6): 3070-3102.

Shih and Chen (2025) Measuring multivariate regression association via spatial sign (in revision, Computational Statistics & Data Analysis)

See Also

mram

Examples

n = 200
p = 10

x_data = matrix(rnorm(p*n),n,p)
y_data = x_data[,1]*x_data[,2]+x_data[,1]-x_data[,3]+rnorm(n)
colnames(x_data) = paste0(rep("X",p),seq(1,p))

library(MRAM)
mram_res = vs_mram(y_data,x_data)

[Package MRAM version 0.1.2 Index]