mca.eigen.fix: mca.eigen.fix

In ExPosition: Exploratory Analysis with the Singular Value Decomposition

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" = Benzécri correction, "bg" = Greenacre adjustment to Benzécri 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.

Author(s)

Derek Beaton

References

Benzécri, J. P. (1979). Sur le calcul des taux d'inertie dans l'analyse d'un questionnaire. Cahiers de l'Analyse des Données, 4, 377-378.
Greenacre, M. J. (2007). Correspondence Analysis in Practice. Chapman and Hall.

See Also

epMCA

Examples

	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 Benzécri correction		
	mca.wine.res.b <- mca.eigen.fix(data, expo.output,correction=c('b'))
	#mca.eigen.fix with Benzécri + Greenacre adjustment	
	mca.wine.res.bg <- mca.eigen.fix(data,expo.output,correction=c('b','g'))

Example output

Loading required package: prettyGraphs
dev.new(): using pdf(file="Rplots1.pdf")
dev.new(): using pdf(file="Rplots2.pdf")
dev.new(): using pdf(file="Rplots3.pdf")

ExPosition documentation built on May 1, 2019, 7:06 p.m.