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 |
x_data |
A |
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
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)