# shrinkage: Data set illustrating shrinkage In languageR: Analyzing Linguistic Data: A Practical Introduction to Statistics

## Description

Simulated data set for illustrating shrinkage.

## Usage

 `1` ```data(shrinkage) ```

## Format

A data frame with 200 observations on the following 6 variables.

`intercept`

a numeric vector for the intercept.

`frequency`

a numeric vector for word frequency.

`subject`

a factor for subjects with levels `S1`, `S2`, ... , `S10`.

`error`

a numeric vector for residuals.

`ranef`

a numeric vector for random effect.

`RT`

a numeric vector for simulated RTs.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38``` ```## 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) ```

languageR documentation built on May 2, 2019, 10:02 a.m.