lmrse: Linear regression model with cluster robust standard errors

In jrs95/lmrse: Linear regression with clustered robust standard errors

Linear regression model with cluster robust standard errors

Description

lmrse fits a linear regression model with cluster robust standard errors for all markers.

Usage

lmrse(formula, cluster, data = NULL)

Arguments

formula	containing the marker matrix as the response and the exposure and covariates as the dependent terms
cluster	clustering variable
data	an optional data.frame which contains the covariates specified in the formula

Value

lmrse returns a list of coefficients, standard errors and p-values matrices:

coef	a matrix of regression coefficients
se	a matrix of standard errors
p	a matrix of p-values

Author(s)

James Staley [e-mail]

Examples

# Data
y <- rnorm(5000000)
y <- matrix(y, ncol = 1000)
colnames(y) <- paste0("var", 1:1000)
x <- rnorm(5000)
cluster <- rep(1:1000, 5)
c1 <- rbinom(5000, 1, 0.5)
c2 <- rnorm(5000)

# Analyses
res <- lmrse(y ~ x + c1 + c2, cluster = cluster)

jrs95/lmrse documentation built on Jan. 15, 2024, 3:48 a.m.