mv_sis: Feature screening via MV Index

In semidist: Measure Dependence Between Categorical and Continuous Variables

Feature screening via MV Index

Description

Implement the feature screening for the classification problem via MV index.

Usage

mv_sis(X, y, d = NULL, parallel = FALSE)

Arguments

X	Data of multivariate covariates, which should be an n-by-p matrix.
y	Data of categorical response, which should be a factor of length n.
d	An integer specifying how many features should be kept after screening. Defaults to NULL. If NULL, then it will be set as [n / log(n)], where [x] denotes the integer part of x.
parallel	A boolean indicating whether to calculate parallelly via furrr::future_map. Defaults to FALSE.

Value

A list of the objects about the implemented feature screening:

• measurement: sample MV index calculated for each single covariate;

• selected: indicies or names (if avaiable as colnames of X) of covariates that are selected after feature screening;

• ordering: order of the calculated measurements of each single covariate. The first one is the largest, and the last is the smallest.

Examples

X <- mtcars[, c("mpg", "disp", "hp", "drat", "wt", "qsec")]
y <- factor(mtcars[, "am"])

mv_sis(X, y, d = 4)

semidist documentation built on Nov. 21, 2023, 9:06 a.m.