| splitplot | R Documentation |
Simulated lexical decision latencies with priming as treatment and reaction time in lexical decision as dependent variable.
data(splitplot)
A data frame with 800 observations on the following 11 variables.
itemsA factor with levels w1, w2,
..., w40, coding 40 word items.
ritemsThe by-word random adjustments to the intercept.
listA factor with levels listA and listB.
The priming effect is counterbalanced for subjects across these
two lists, compare table(splitplot$list, splitplot$subjects).
rlistThe by-list random adjustments to the intercept.
primingA treatment factor with levels primed and
unprimed.
fprimingThe priming effect, -30 for the primed and 0 for the unprimed condition.
subjectsA factor with levels s1, s2,
... s20 coding 20 subjects.
rsubjectThe by-subject random adjustments to the intercept.
errorThe by-observation noise.
intThe intercept.
RTThe reaction time.
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.
## 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)
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