1
$\begingroup$

Is there a way to carry a variance component analysis using nlme or lme4 packages and how would I calculate the percentage of variance that is attributable to the random effects?

For example, my output from lme is:

Random effects:
 Formula: ~1 | Group
    (Intercept)
StdDev:   0.6592846

 Formula: ~1 | Test %in% Group
(Intercept)
StdDev:    227.5543

 Formula: ~1 | Person %in% Test %in% Group
        (Intercept) Residual
StdDev:    388.7217 40.67243

Thank you.

$\endgroup$
1
$\begingroup$

A variance component analysis (VCA) with lme4 could look like this.

Consider this dataset, where measurements on 10 devices, for 5 days each with two replicates on each day were performed for assessing the total variability and the contribution of 3 variance components (device, day, error):

> dat <- data.frame(y=50+rep(rnorm(10,,2.5), rep(10,10))+rep(rnorm(50,,2), rep(2,50))+rnorm(100,,1.5), device=gl(10,10), day=gl(5,2,100))
> fit <- lmer(y~(1|device)+(1|device:day), dat)
> sum.fit <- summary(fit)
> vc.tab <- as.data.frame(sum.fit$varcor)
    > vc.tab$CV <- vc.tab$sdcor*100/mean(dat$y)
> vc.tab$Perc <- paste(round(vc.tab$vcov/sum(vc.tab$vcov)*100, 2),"%", sep="")
> vc.tab <- vc.tab[,-c(2:3)]
> vc.tab
         grp     vcov    sdcor       CV   Perc
1 device:day 5.886198 2.426149 4.779134 58.27%
2     device 1.886688 1.373568 2.705714 18.68%
3   Residual 2.328238 1.525856 3.005699 23.05%

For this (fully) nested model, this is the usual VCA-table. Now, confidence intervals for VC and total variance are the next step.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.