Linear regression model with cluster robust standard errors — lmrse • lmrse

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

James Staley jrstaley95@gmail.com

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)