| english {languageR} | R Documentation |
This data set gives mean visual lexical decision latencies and word naming latencies to 2284 monomorphemic English nouns and verbs, averaged for old and young subjects, with various predictor variables.
data(english)
A data frame with 4568 observations on the following variables.
RTlexdecRTnamingFamiliarityWordAgeSubjectyoung versus old.WordCategoryN (noun) and V (verb).WrittenFrequencyWrittenSpokenFrequencyRatioFamilySizeDerivationalEntropyInflectionalEntropyNumberSimplexSynsetsNumberComplexSynsetsLengthInLettersNcountMeanBigramFrequencyFrequencyInitialDiphoneConspelVConspelNConphonVConphonNConfriendsVConfriendsNConffVConffNConfbVConfbNNounFrequencyVerbFrequencyCVC) or a vowel (V).Obstruentcont) or an obstruent (obst).Fricationburst) or frication (frication) for
consonant-initial words, and for vowel-initial words whether the vowel is
long or short.Voicevoiced
or voiceless.FrequencyInitialDiphoneWordFrequencyInitialDiphoneSyllableCorrectLexdecBalota, D.A., Cortese, M.J. and Pilotti, M. (1999) Visual lexical decision latencies for 2906 words. Available at http://www.artsci.wustl.edu/~dbalota/lexical_decision.html.
Spieler, D. H. and Balota, D. A. (1998) Naming latencies for 2820 words, http://www.artsci.wustl.edu/~dbalota/naming.html.
Balota, D., Cortese, M., Sergent-Marshall, S., Spieler, D. and Yap, M. (2004) Visual word recognition for single-syllable words, Journal of Experimental Psychology:General, 133, 283-316.
Baayen, R.H., Feldman, L. and Schreuder, R. (2006) Morphological influences on the recognition of monosyllabic monomorphemic words, Journal of Memory and Language, 53, 496-512.
data(english) # ---- orthogonalize orthographic consistency measures items = english[english$AgeSubject == "young",] items.pca = prcomp(items[ , c(18:27)], center = TRUE, scale = TRUE) x = as.data.frame(items.pca$rotation[,1:4]) items$PC1 = items.pca$x[,1] items$PC2 = items.pca$x[,2] items$PC3 = items.pca$x[,3] items$PC4 = items.pca$x[,4] items2 = english[english$AgeSubject != "young", ] items2$PC1 = items.pca$x[,1] items2$PC2 = items.pca$x[,2] items2$PC3 = items.pca$x[,3] items2$PC4 = items.pca$x[,4] english = rbind(items, items2) # ---- add Noun-Verb frequency ratio english$NVratio = log(english$NounFrequency+1)-log(english$VerbFrequency+1) # ---- build model with ols() from Design library(Design) english.dd = datadist(english) options(datadist = 'english.dd') english.ols = ols(RTlexdec ~ Voice + PC1 + MeanBigramFrequency + rcs(WrittenFrequency, 5) + rcs(WrittenSpokenFrequencyRatio, 3) + NVratio + WordCategory + AgeSubject + rcs(FamilySize, 3) + InflectionalEntropy + NumberComplexSynsets + rcs(WrittenFrequency, 5) : AgeSubject, data = english, x = TRUE, y = TRUE) # ---- plot partial effects par(mfrow = c(4, 3), mar = c(4, 4, 1, 1), oma = rep(1, 4)) plot(english.ols, adj.subtitle = FALSE, ylim = c(6.4, 6.9), conf.int = FALSE) par(mfrow = c(1, 1)) # ---- validate the model validate(english.ols, bw = TRUE, B = 200)