lexicalMeasuresClasses: Classification of lexical measures
In languageR: Analyzing Linguistic Data: A Practical Introduction to Statistics

Description Usage Format References Examples

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

1	data(lexicalMeasuresClasses)

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.

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.

## 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

## End(Not run)