splitplot {languageR}R Documentation

Simulated data set with split plot design

Description

Simulated lexical decision latencies with priming as treatment and reaction time in lexical decision as dependent variable.

Usage

data(splitplot)

Format

A data frame with 800 observations on the following 11 variables.

items
A factor with levels w1, w2, ..., w40, coding 40 word items.
ritems
The by-word random adjustments to the intercept.
list
A factor with levels listA and listB. The priming effect is counterbalanced for subjects across these two lists, compare table(splitplot$list, splitplot$subjects).
rlist
The by-list random adjustments to the intercept.
priming
A treatment factor with levels primed and unprimed.
fpriming
The priming effect, -30 for the primed and 0 for the unprimed condition.
subjects
A factor with levels s1, s2, ... s20 coding 20 subjects.
rsubject
The by-subject random adjustments to the intercept.
error
The by-observation noise.
int
The intercept.
RT
The reaction time.

Source

R. H. Baayen, D. J. Davidson and D. M. Bates. Mixed-effects modeling with crossed random effects for subjects and items. Manuscript under revision for Journal of Memory and Language.

Examples

## Not run: 
data(splitplot)
table(splitplot$list, splitplot$subjects)
library(lme4)
dat.lmer1 = lmer2(RT ~ list*priming+(1+priming|subjects)+(1+list|items),data=dat)
dat.lmer2 = lmer2(RT ~ list*priming+(1+priming|subjects)+(1|items),data=dat)
dat.lmer3 = lmer2(RT ~ list*priming+(1|subjects)+(1|items),data=dat)
dat.lmer4 = lmer2(RT ~ list*priming+(1|subjects),data=dat)
anova(dat.lmer1, dat.lmer2, dat.lmer3, dat.lmer4)
print(dat.lmer3, corr=FALSE)
## End(Not run)

[Package languageR version 0.953 Index]