Data set illustrating shrinkage
Simulated data set for illustrating shrinkage.
data(shrinkage)
A data frame with 200 observations on the following 6 variables.
intercepta numeric vector for the intercept.
frequencya numeric vector for word frequency.
subjecta factor for subjects with levels S1,
S2, ... , S10.
errora numeric vector for residuals.
ranefa numeric vector for random effect.
RTa numeric vector for simulated RTs.
## Not run:
data(shrinkage)
require(lme4)
require(lmerTest)
require(optimx)
shrinkage.lmer = lmer(RT ~ frequency + (1|subject),
data = shrinkage,
control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb"))
shrinkage.lmList = lmList(RT ~ frequency | subject, data = shrinkage)
# and visualize the difference between random regression
# and mixed-effects regression
mixed = coef(shrinkage.lmer)[[1]]
random = coef(shrinkage.lmList)
subj = unique(shrinkage[,c("subject", "ranef")])
subj = subj[order(subj$subject),]
subj$random = random[,1]
subj$mixed = mixed[,1]
subj = subj[order(subj$random),]
subj$rank = 1:nrow(subj)
par(mfrow=c(1,2))
plot(subj$rank, subj$random, xlab="rank", ylab="RT", ylim=c(200,550), type="n")
text(subj$rank, subj$random, as.character(subj$subject), cex=0.8, col="red")
mtext("random regression", 3, 1)
points(subj$rank, 400+subj$ranef, col="blue")
abline(h=400)
plot(subj$rank, subj$mixed, xlab="rank", ylab="RT", ylim=c(200,550), type="n")
text(subj$rank, subj$mixed, as.character(subj$subject), cex=0.8, col = "red")
mtext("mixed-effects regression", 3, 1)
points(subj$rank, 400+subj$ranef, col="blue")
abline(h=400)
par(mfrow=c(1,1))
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.