| 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.
SubjectWordRTRootFrequencyConditionbaseheid (neologism is preceded 40 trials back
by its base word) and heidheid (the neologism is preceded
40 trials back by itself).RatingFrequencyBaseFrequencyLengthInLettersFamilySizeNumberOfSynsetsRT4WordsBackRT3WordsBackRT2WordsBackRT1WordBackRT1WordLaterRT2WordsLaterRTtoPrimeDe 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)