Ratings for 81 English nouns
Subjective frequency ratings, ratings of estimated weight, and ratings of estimated size, averaged over subjects, for 81 concrete English nouns.
data(ratings)
A data frame with 81 observations on the following 14 variables.
Worda factor with words as levels.
Frequencya numeric vector of logarithmically transformed frequencies
FamilySizea numeric vector of logarithmically transformed morphological family sizes.
SynsetCounta numeric vector with logarithmically transformed counts of the number of synonym sets in WordNet in which the word is listed.
Lengtha numeric vector for the length of the word in letters.
Classa factor with levels animal and plant.
FreqSingulara numeric vector for the frequency of the word in the singular.
FreqPlurala numeric vector with the frequency of the word in the plural.
DerivEntropya numeric vector with the derivational entropies of the words.
Complexa factor coding morphological complexity with levels
complex and simplex.
rInfla numeric vector coding the log of ratio of singular to plural frequencies.
meanWeightRatinga numeric vector for the estimated weight of the word's referent, averaged over subjects.
meanSizeRatinga numeric vector for the estimated size of the word's referent, averaged over subjects.
meanFamiliaritya numeric vector with subjective frequency estimates, averaged over subjects.
Data collected together with Jen Hay at the University of Canterbury, Christchurch, New Zealand, 2004.
## Not run: data(ratings) ratings.lm = lm(meanSizeRating ~ meanFamiliarity * Class + I(meanFamiliarity^2), data = ratings) ratings$fitted = fitted(ratings.lm) plot(ratings$meanFamiliarity, ratings$meanSizeRating, xlab = "mean familiarity", ylab = "mean size rating", type = "n") text(ratings$meanFamiliarity, ratings$meanSizeRating, substr(as.character(ratings$Class), 1, 1), col = 'darkgrey') plants = ratings[ratings$Class == "plant", ] animals = ratings[ratings$Class == "animal", ] plants = plants[order(plants$meanFamiliarity),] animals = animals[order(animals$meanFamiliarity),] lines(plants$meanFamiliarity, plants$fitted) lines(animals$meanFamiliarity, animals$fitted) ## End(Not run)
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