ExPosition (version 2.8.23)

epCA: epCA: Correspondence Analysis (CA) via ExPosition.

Description

Correspondence Analysis (CA) via ExPosition.

Usage

epCA(DATA, DESIGN = NULL, make_design_nominal = TRUE, masses = NULL, weights = NULL,
	hellinger = FALSE, symmetric = TRUE, graphs = TRUE, k = 0)

Arguments

DATA

original data to perform a CA on.

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 (default) symmetric factor scores for rows and columns are computed. If FALSE, the simplex (column-based) will be returned.

graphs

a boolean. If TRUE (default), graphs and plots are provided (via epGraphs)

k

number of components to return.

Value

See coreCA for details on what is returned.

Details

epCA performs correspondence analysis. Essentially, a PCA for qualitative data (frequencies, proportions). If you decide to use Hellinger distance, it is best to set symmetric to FALSE.

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. Greenacre, M. J. (2007). Correspondence Analysis in Practice. Chapman and Hall.

See Also

coreCA, epMCA

Examples

# NOT RUN {
	data(authors)
	ca.authors.res <- epCA(authors$ca$data)
# }