languageR (version 1.5.0)

lexicalMeasuresClasses: Classification of lexical measures

Description

A data frame labelling the lexical measures in the dataset lexicalMeasures as measures of form or meaning.

Usage

data(lexicalMeasuresClasses)

Format

A data frame with 23 observations on the following 3 variables.

Variable

a factor with as levels the measures:

Bigr

Mean Bigram Frequency.

CelS

CELEX Frequency.

Dent

Derivational Entropy.

fbN

Token Count of Backward Inconsistent Words.

fbV

Type Count of Backward Inconsistent Words.

Fdif

Ratio of Frequencies in Written and Spoken English.

ffN

Token Count of Forward Inconsistent Words.

ffNonzero

Type Count of Forward Inconsistent Words with Nonzero Frequency.

ffV

Type Count of Forward Inconsistent Words

friendsN

Token Count of Consistent Words.

friendsV

Type Count of Consistent Words.

Ient

Inflectional Entropy

InBi

Initial Bigram Frequency

Len

Length in Letters

Ncou

Orthographic Neighborhood Density

NsyC

Number of Complex Synsets

NsyS

Number of Simplex Synsets

NVratio

Ratio of Noun and Verb Frequencies

phonN

Token Count of Phonological Neighbors.

phonV

Type Count of Phonological Neighbors.

spelN

Token Count of Orthographic Neighbors.

spelV

Type Count of Orthographic Neighbors.

Vf

Morphological Family Size.

Class

a factor with levels Form and Meaning.

Explanation

a factor with glosses for the variables.

References

Baayen, R.H., Feldman, L. and Schreuder, R. (2006) Morphological influences on the recognition of monosyllabic monomorphemic words, Journal of Memory and Language, 53, 496-512.

Examples

# NOT RUN {
library(cluster)
data(lexicalMeasures)
data(lexicalMeasuresClasses)

lexicalMeasures.cor = cor(lexicalMeasures[,-1], method = "spearman")^2
x = data.frame(measure = rownames(lexicalMeasures.cor), 
cluster = cutree(diana(dist(lexicalMeasures.cor)), 5),
class = lexicalMeasuresClasses$Class)
x = x[order(x$cluster), ]
x
# }