univariate_regression: Perform Univariate Linear Regression Separately for Columns...

In susieR: Sum of Single Effects Linear Regression

Perform Univariate Linear Regression Separately for Columns of X

Description

This function performs the univariate linear regression y ~ x separately for each column x of X. Each regression is implemented using .lm.fit(). The estimated effect size and stardard error for each variable are outputted.

Usage

univariate_regression(
  X,
  y,
  Z = NULL,
  center = TRUE,
  scale = FALSE,
  return_residuals = FALSE
)

Arguments

X	n by p matrix of regressors.
y	n-vector of response variables.
Z	Optional n by k matrix of covariates to be included in all regresions. If Z is not NULL, the linear effects of covariates are removed from y first, and the resulting residuals are used in place of y.
center	If center = TRUE, center X, y and Z.
scale	If scale = TRUE, scale X, y and Z.
return_residuals	Whether or not to output the residuals if Z is not NULL.

Value

A list with two vectors containing the least-squares estimates of the coefficients (betahat) and their standard errors (sebetahat). Optionally, and only when a matrix of covariates Z is provided, a third vector residuals containing the residuals is returned.

Examples

set.seed(1)
n = 1000
p = 1000
beta = rep(0,p)
beta[1:4] = 1
X = matrix(rnorm(n*p),nrow = n,ncol = p)
X = scale(X,center = TRUE,scale = TRUE)
y = drop(X %*% beta + rnorm(n))
res = univariate_regression(X,y)
plot(res$betahat/res$sebetahat)

susieR documentation built on March 7, 2023, 6:11 p.m.