epMCA: epMCA: Multiple Correspondence Analysis (MCA) via ExPosition.

In ExPosition: Exploratory Analysis with the Singular Value Decomposition

Description

Multiple Correspondence Analysis (MCA) via ExPosition.

Usage

epMCA(DATA, make_data_nominal = TRUE, DESIGN = NULL, make_design_nominal = TRUE, 
	masses = NULL, weights = NULL, hellinger = FALSE, 
	symmetric = TRUE, correction = c("b"), graphs = TRUE, k = 0)

Arguments

DATA	original data to perform a MCA on. This data can be in original formatting (qualitative levels) or in dummy-coded variables.
make_data_nominal	a boolean. If TRUE (default), DATA is recoded as a dummy-coded matrix. If FALSE, DATA is a dummy-coded matrix.
DESIGN	a design matrix to indicate if rows belong to groups.
make_design_nominal	a boolean. If TRUE (default), DESIGN is a vector that indicates groups (and will be dummy-coded). If FALSE, DESIGN is a dummy-coded matrix.
masses	a diagonal matrix or column-vector of masses for the row items.
weights	a diagonal matrix or column-vector of weights for the column it
hellinger	a boolean. If FALSE (default), Chi-square distance will be used. If TRUE, Hellinger distance will be used.
symmetric	a boolean. If TRUE symmetric factor scores for rows.
correction	which corrections should be applied? "b" = Benzécri correction, "bg" = Greenacre adjustment to Benzécri correction.
graphs	a boolean. If TRUE (default), graphs and plots are provided (via epGraphs)
k	number of components to return.

Details

epMCA performs multiple correspondence analysis. Essentially, a CA for categorical data.
It should be noted that when hellinger is selected as TRUE, no correction will be performed. Additionally, if you decide to use Hellinger, it is best to set symmetric to FALSE.

Value

See coreCA for details on what is returned. In addition to the values returned:

$pdq	this is the corrected SVD data, if a correction was selected. If no correction was selected, it is uncorrected.
$pdq.uncor	uncorrected SVD data.

Author(s)

Derek Beaton

References

Abdi, H., and Williams, L.J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2, 433-459.
Abdi, H., and Williams, L.J. (2010). Correspondence analysis. In N.J. Salkind, D.M., Dougherty, & B. Frey (Eds.): Encyclopedia of Research Design. Thousand Oaks (CA): Sage. pp. 267-278.
Abdi, H. (2007). Singular Value Decomposition (SVD) and Generalized Singular Value Decomposition (GSVD). In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics.Thousand Oaks (CA): Sage. pp. 907-912.
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

coreCA, epCA, mca.eigen.fix

Examples

	data(mca.wine)
	mca.wine.res <- epMCA(mca.wine$data)

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.