selfPacedReadingHeid {languageR} | R Documentation |
Self-paced reading latencies for Dutch neologisms ending in the suffix -heid.
data(selfPacedReadingHeid)
A data frame with 1280 observations on the following 18 variables.
Subject
Word
RT
RootFrequency
Condition
baseheid
(neologism is preceded 40 trials back
by its base word) and heidheid
(the neologism is preceded
40 trials back by itself).Rating
Frequency
BaseFrequency
LengthInLetters
FamilySize
NumberOfSynsets
RT4WordsBack
RT3WordsBack
RT2WordsBack
RT1WordBack
RT1WordLater
RT2WordsLater
RTtoPrime
De Vaan, L., Schreuder, R. and Baayen, R. H. (2007) Regular morphologically complex neologisms leave detectable traces in the mental lexicon, The Mental Lexicon, 2, in press.
## Not run: data(selfPacedReadingHeid) # data validation plot(sort(selfPacedReadingHeid$RT)) selfPacedReadingHeid = selfPacedReadingHeid[selfPacedReadingHeid$RT > 5 & selfPacedReadingHeid$RT < 7.2,] # fitting a mixed-effects model library(lme4, keep.source = FALSE) x = selfPacedReadingHeid[,12:15] x.pr = prcomp(x, center = TRUE, scale = TRUE) selfPacedReadingHeid$PC1 = x.pr$x[,1] selfPacedReadingHeid.lmer = lmer(RT ~ RTtoPrime + LengthInLetters + PC1 * Condition + (1|Subject) + (1|Word), data = selfPacedReadingHeid) pvals.fnc(selfPacedReadingHeid.lmer)$summary # model criticism selfPacedReadingHeid.lmerA = lmer(RT ~ RTtoPrime + LengthInLetters + PC1 * Condition + (1|Subject) + (1|Word), data = selfPacedReadingHeid[abs(scale(resid(selfPacedReadingHeid.lmer))) < 2.5, ]) qqnorm(resid(selfPacedReadingHeid.lmerA)) pvals.fnc(selfPacedReadingHeid.lmerA)$summary ## End(Not run)