run_lmer: A wrapper for deciding either to run a default lm or a...

In syyang93/lmeR: What the Package Does (One Line, Title Case)

Description

formula: Expr ~ SCORE + Cov + (1|Rcov), where Cov = fixed covariates and Rcov = random covariates

Usage

run_lmer(expr, cov, rcov, SCORE, omit.outlier = T, outlier_sd = 3)

Arguments

expr	Expression vector (numeric) with length = #samples (numeric vector)
cov	Regression covariates (fixed effects) in form cov x samples (data frame)
rcov	Regression covariates (random effects) in the form rcov x samples (data frame)
SCORE	mtDNA-CN, not included in cov (numeric vector)
omit.outlier	(no longer supported) Whether or not you want to omit gene expression outliers

Details

Runs linear regression to estimate effect of SCORE on gene expression/metabolite/epigenetic data

Value

A 1 x 6 vector output from an lm() like below: 'intercept', 'beta', 'SE', 't_value', 'pval', 'corr.rho'

Author: Stephanie Yang Adapted from: Vamsee Pillalamarri

syyang93/lmeR documentation built on July 16, 2020, 12:49 a.m.