lmrse

This package is used to fit linear regression models with cluster robust standard errors across high-dimensional phenotypes to assess change over time.

Installation

install.packages("remotes")
remotes::install_github("jrs95/lmrse")

Functions

• lmrse: fits a linear regression model with cluster robust standard errors for all markers.
  
• coerce.lmrse: coerces the lmrse results into a single data.frame.

Example

# Libraries
library(lmrse)

# Data  
y <- rnorm(5000000)
y <- matrix(y, ncol = 1000) # a matrix of phenotypes (rows = individuals, columns = markers)
colnames(y) <- paste0("pheno", 1:1000)
x <- rnorm(5000) # a vector of exposure
cluster <- rep(1:1000, 5) # cluster variable
c1 <- rbinom(5000, 1, 0.5) # covariate 1
c2 <- rnorm(5000) # covariate 2

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

Citation

Staley JR et al. Longitudinal analysis strategies for modelling epigenetic trajectories. Int J Epidemiol 2018;47(2):516-525.