normalize: Normalize Response and Covariates

In BayesianLasso: Bayesian Lasso Regression and Tools for the Lasso Distribution

Normalize Response and Covariates

Description

This function centers and (optionally) scales the response vector and each column of the design matrix using the population variance. It is used to prepare data for Bayesian Lasso regression.

Usage

normalize(y, X, scale = TRUE)

Arguments

y	A numeric response vector.
X	A numeric matrix or data frame of covariates (design matrix).
scale	Logical; if TRUE, variables are scaled to have unit population variance (default is TRUE).

Value

A list with the following elements:

• vy: Normalized response vector.

• mX: Normalized design matrix.

• mu.y: Mean of the response vector.

• sigma2.y: Population variance of the response vector.

• mu.x: Vector of column means of X.

• sigma2.x: Vector of population variances for columns of X.

Examples

set.seed(1)
X <- matrix(rnorm(100 * 10), 100, 10)
beta <- c(2, -3, rep(0, 8))
y <- as.vector(X %*% beta + rnorm(100))
norm_result <- normalize(y, X)

BayesianLasso documentation built on Aug. 8, 2025, 7:33 p.m.