Description Usage Format Source Examples
Simulated lexical decision latencies with priming as treatment and reaction time in lexical decision as dependent variable.
1 |
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.
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ## 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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