mca.eigen.fix: mca.eigen.fix

Description

A function for correcting the eigenvalues and output from multiple correspondence analysis (MCA, epMCA)

Usage

mca.eigen.fix(DATA, mca.results, make_data_nominal = TRUE, numVariables = NULL, 
correction = c("b"), symmetric = FALSE)

Arguments

DATA

original data (i.e., not transformed into disjunctive coding)

mca.results

output from epMCA

make_data_nominal

a boolean. Should DATA be transformed into disjunctive coding? Default is TRUE.

numVariables

the number of actual measures/variables in the data (typically the number of columns in DATA)

correction

which corrections should be applied? "b" = Benz<U+00E9>cri correction, "bg" = Greenacre adjustment to Benz<U+00E9>cri correction.

symmetric

a boolean. If the results from MCA are symmetric or asymmetric factor scores. Default is FALSE.

Value

mca.results

a modified version of mca.results. Factor scores (e.g., $fi, $fj), and $pdq are updated based on corrections chosen.

References

Benz<U+00E9>cri, J. P. (1979). Sur le calcul des taux d'inertie dans l'analyse d'un questionnaire. Cahiers de l'Analyse des Donn<U+00E9>es, 4, 377-378. Greenacre, M. J. (2007). Correspondence Analysis in Practice. Chapman and Hall.

Examples

Run this code

# NOT RUN {
	data(mca.wine)
	#No corrections used in MCA
	mca.wine.res.uncor <- epMCA(mca.wine$data,correction=NULL)
	data <- mca.wine$data
	expo.output <- mca.wine.res.uncor$ExPosition.Data
	#mca.eigen.fix with just Benz<U+00E9>cri correction		
	mca.wine.res.b <- mca.eigen.fix(data, expo.output,correction=c('b'))
	#mca.eigen.fix with Benz<U+00E9>cri + Greenacre adjustment	
	mca.wine.res.bg <- mca.eigen.fix(data,expo.output,correction=c('b','g'))
# }