ratings: Ratings for 81 English nouns

In languageR: Data Sets and Functions with Analyzing Linguistic Data: A Practical Introduction to Statistics

Ratings for 81 English nouns

Description

Subjective frequency ratings, ratings of estimated weight, and ratings of estimated size, averaged over subjects, for 81 concrete English nouns.

Usage

data(ratings)

Format

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.

Source

Data collected together with Jen Hay at the University of Canterbury, Christchurch, New Zealand, 2004.

Examples

## 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)

languageR documentation built on June 10, 2025, 9:08 a.m.