epCA {ExPosition} R Documentation

## 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.

### 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.

### Value

See coreCA for details on what is returned.

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

coreCA, epMCA
	data(authors)
ca.authors.res <- epCA(authors$ca$data)