splitplot {languageR}

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)
dat=splitplot
require(lme4)
require(optimx)
require(lmerTest)
dat.lmer1 = lmer(RT ~ list*priming+(1+priming|subjects)+(1+list|items),data=dat,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
dat.lmer2 = lmer(RT ~ list*priming+(1+priming|subjects)+(1|items),data=dat,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
dat.lmer3 = lmer(RT ~ list*priming+(1|subjects)+(1|items),data=dat,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
dat.lmer4 = lmer(RT ~ list*priming+(1|subjects),data=dat,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
anova(dat.lmer1, dat.lmer2, dat.lmer3, dat.lmer4)
summary(dat.lmer3)

## End(Not run)