Description Usage Format Source Examples
Subjective frequency ratings, ratings of estimated weight, and ratings of estimated size, averaged over subjects, for 81 concrete English nouns.
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A data frame with 81 observations on the following 14 variables.
Word
a factor with words as levels.
Frequency
a numeric vector of logarithmically transformed frequencies
FamilySize
a numeric vector of logarithmically transformed morphological family sizes.
SynsetCount
a numeric vector with logarithmically transformed counts of the number of synonym sets in WordNet in which the word is listed.
Length
a numeric vector for the length of the word in letters.
Class
a factor with levels animal
and plant
.
FreqSingular
a numeric vector for the frequency of the word in the singular.
FreqPlural
a numeric vector with the frequency of the word in the plural.
DerivEntropy
a numeric vector with the derivational entropies of the words.
Complex
a factor coding morphological complexity with levels
complex
and simplex
.
rInfl
a numeric vector coding the log of ratio of singular to plural frequencies.
meanWeightRating
a numeric vector for the estimated weight of the word's referent, averaged over subjects.
meanSizeRating
a numeric vector for the estimated size of the word's referent, averaged over subjects.
meanFamiliarity
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ## 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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