lexicalMeasuresClasses {languageR} | R Documentation |
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
## End(Not run)
[Package
languageR version 0.953
Index]