inst/doc/theory_practice_random_effects.R

In variancePartition: Quantify and interpret divers of variation in multilevel gene expression experiments

## ----knitr, echo=FALSE, results='hide'------------------------------------------------------------
library("knitr")
opts_chunk$set(tidy=FALSE,dev="pdf",fig.show="hide",
               fig.width=4,fig.height=4.5,
               message=FALSE, warning=FALSE)


## ----style, eval=TRUE, echo=FALSE, results="asis"--------------------------
BiocStyle::latex()

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options(digits=3, width=80, prompt=" ", continue=" ")
opts_chunk$set(comment=NA, fig.width=7, fig.height=7)

## ----code, cache=TRUE---------------------------------------------------------
library('variancePartition')
library('lme4')
library('r2glmm')

set.seed(1)

N = 1000
beta = 3
alpha = c(1, 5, 7)

# generate 1 fixed variable and 1 random variable with 3 levels
data = data.frame(X=rnorm(N), Subject = sample(c('A', 'B', 'C'), 100, replace=TRUE))

# simulate variable
# y = X\beta + Subject\alpha + \sigma^2
data$y = data$X*beta + model.matrix(~ data$Subject) %*% alpha  + rnorm(N, 0, 1)

# fit model
fit = lmer( y ~ X +(1|Subject), data, REML=FALSE)

# calculate variance fraction using variancePartition
# include the total sum in the denominator
frac = calcVarPart(fit)
frac

# the variance fraction excluding the random effect from the denominator
# is the same as from r2glmm
frac[['X']] / (frac[['X']] + frac[['Residuals']])

# using r2glmm
r2beta(fit)

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options(prompt="> ", continue="+ ")