LexCA {Xplortext}

Correspondence Analysis of a Lexical Table from a TextData object (LexCA)

Description

Performs Correspondence Analysis on the working lexical table contained in TextData object. Supplementary documents, words, segments, contextual quantitative and qualitative variables can be considered if previously selected in TextData function.

Usage

LexCA(object, ncp=5, context.sup="ALL", doc.sup=NULL, word.sup=NULL, 
  segment=FALSE, graph=TRUE, axes=c(1, 2), lmd=3, lmw=3)

Arguments

object

object of TextData class

ncp

number of dimensions kept in the results (by default 5)

context.sup

column index(es) or name(s) of the contextual qualitative or quantitative variables among those selected in TextData function (by default "ALL")

doc.sup

vector indicating the index(es) or name(s) of the supplementary documents (rows) (by default NULL)

word.sup

vector indicating the index(es) or name(s) of the supplementary words (columns) (by default NULL)

segment

if TRUE, the repeated segments identified by TextData function will be considered as supplementary columns (by default FALSE)

graph

if TRUE, basic graphs are displayed; use plot.LexCA to obtain more graphs (by default TRUE)

axes

length-2 vector indicating the axes to plot (by default axes=c(1,2))

lmd

only the documents whose contribution is over lmd times the average-document-contribution are plotted (by default lmd=3)

lmw

only the words whose contribution is over lmw times the average-word-contribution are plotted (by default lmw=3)

Details

In the case of a direct CA, DocTerm is a non-aggregate table and:

1. the contextual quantitative variables are considered as supplementary quantitative columns in CA.

2. the categories of the contextual qualitative variables are considered as supplementary columns in CA.

In the case of an aggregate CA, DocTerm is an aggregate table and:

1. the contextual quantitative variables are considered as supplementary quantitative columns in CA; the value of an active aggregate-document for a variable is the mean of the values corresponding to the source-documents belonging to this aggregate-document.

2. the categories of the contextual qualitative variables are threatened as supplementary rows in CA; these rows contain the frequency with which each the set of documents belonging to this category has used the different words.

Value

Returns a list including:

eig

matrix with the eigenvalues, the percentages of inertia and the cumulative percentages of inertia

row

list of matrices with all the results for the documents (coordinates, square cosines, contributions, inertia)

col

list of matrices with all the results for the words (coordinates, square cosines, contributions, inertia)

row.sup

if row.sup is non-NULL, list of matrices with all the results for the supplementary documents (coordinates, square cosines)

col.sup

if col.sup is non-NULL, list of matrices with all the results for the supplementary words (coordinates, square cosines)

quanti.sup

if quanti.sup is non-NULL, list of matrices containing the results for the supplementary quantitative variables (coordinates, square cosines)

quali.sup

if quali.sup is non-NULL, list of matrices with all the results for the supplementary categorical variables; see section details

meta

list of the documents/words whose contribution is over lmd/lmw times the average document/word contribution

VCr

Cramer's V coefficient

Inertia

total inertia

info

information about the corpus

segment

if segment is TRUE, list of matrices with the results for the repeated segments (coordinates, square cosines)

var.agg

name of the aggregation variable in the case of an aggregate correspondence analysis

call

a list with some statistics

Author(s)

Ramón Alvarez-Esteban [e-mail], Mónica Bécue-Bertaut, Josep-Anton Sánchez-Espigares

References

Benzécri, J, P. (1981). Pratique de l'analyse des donnees. Linguistique & lexicologie (Vol.3). (P. Dunod., Ed).

Husson F., Lê S., Pagès J. (2011). Exploratory Multivariate Analysis by Example Using R. Chapman & Hall/CRC. doi:10.1201/b10345.

Lebart, L., Salem, A., & Berry, L. (1998). Exploring textual data. (D. Kluwer, Ed.). doi:10.1007/978-94-017-1525-6.

Murtagh F. (2005). Correspondence Analysis and Data Coding with R and Java. Chapman & Hall/CRC.

See Also

TextData, print.LexCA, plot.LexCA, summary.LexCA, ellipseLexCA

Examples

data(open.question)
## Not run: 
### non-aggregate CA
res.TD<-TextData(open.question, var.text=c(9,10), Fmin=10, Dmin=10,
        remov.number=TRUE, stop.word.tm=TRUE)
res.LexCA<-LexCA(res.TD, lmd=0, lmw=1)

## End(Not run)

### aggregate CA
res.TD<-TextData(open.question, var.text=c(9,10), var.agg="Age_Group", Fmin=10, Dmin=10,
        remov.number=TRUE, stop.word.tm=TRUE)
res.LexCA<-LexCA(res.TD, lmd=0, lmw=1)