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

In ZikunY/CARMA: CAusal Robust Mapping Method with Annotations (CARMA)

Perform Univariate Linear Regression Separately for Columns of X

Description

This is a function provided in the package of "susieR", Wang et al (2020) <doi:10.1101/501114>, for performing the univariate linear regression y ~ x separately for each column x of X to generate summary statistics. Each regression is implemented using .lm.fit(). The estimated effect size and stardard error for each variable are outputted.

Usage

compute_summary_statistics(X, y, Z = NULL, center = TRUE,
                                  scale = TRUE, 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.

Details

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

# Example
set.seed(1)
n = 400
p = 500
beta = rep(0,p)
beta[1] = 1
X = matrix(rnorm(n*p),nrow = n,ncol = p)
X = scale(X,center = TRUE,scale = TRUE)
y = drop(X %*% beta + rnorm(n))
SS=compute_summary_statistics(X,y)

ZikunY/CARMA documentation built on Oct. 20, 2024, 8:22 p.m.