ratings: Ratings for 81 English nouns

In languageR: Analyzing Linguistic Data: A Practical Introduction to Statistics

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 May 2, 2019, 10:02 a.m.