Subjective frequency ratings, ratings of estimated weight, and ratings of estimated size, averaged over subjects, for 81 concrete English nouns.
A data frame with 81 observations on the following 14 variables.
a factor with words as levels.
a numeric vector of logarithmically transformed frequencies
a numeric vector of logarithmically transformed morphological family sizes.
a numeric vector with logarithmically transformed counts of the number of synonym sets in WordNet in which the word is listed.
a numeric vector for the length of the word in letters.
a factor with levels
a numeric vector for the frequency of the word in the singular.
a numeric vector with the frequency of the word in the plural.
a numeric vector with the derivational entropies of the words.
a factor coding morphological complexity with levels
a numeric vector coding the log of ratio of singular to plural frequencies.
a numeric vector for the estimated weight of the word's referent, averaged over subjects.
a numeric vector for the estimated size of the word's referent, averaged over subjects.
a numeric vector with subjective frequency estimates, averaged over subjects.
Data collected together with Jen Hay at the University of Canterbury, Christchurch, New Zealand, 2004.
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## 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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