tepDICA {TExPosition}

Discriminant Correspondence Analysis

Description

Discriminant Correspondence Analysis (DICA) via TExPosition.

Usage

tepDICA(DATA, make_data_nominal = FALSE, DESIGN = NULL, make_design_nominal = TRUE, 
group.masses = NULL, weights = NULL, symmetric = TRUE, graphs = TRUE, k = 0)

Arguments

DATA

original data to perform a DICA on. Data can be contingency (like CA) or categorical (like MCA).

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. Required for DICA.

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.

group.masses

a diagonal matrix or column-vector of masses for the groups.

weights

a diagonal matrix or column-vector of weights for the column it

symmetric

a boolean. If TRUE (default) symmetric factor scores for rows.

graphs

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

k

number of components to return.

Details

If you use Hellinger distance, it is best to set symmetric to FALSE.

Note: DICA is a special case of PLS-CA (tepPLSCA) wherein DATA1 are data and DATA2 are a group-coded disjunctive matrix.

Value

See epCA (and also coreCA) for details on what is returned. In addition to the values returned:

fii

factor scores computed for supplemental observations

dii

squared distances for supplemental observations

rii

cosines for supplemental observations

assign

a list of assignment data. See fii2fi and R2

lx

latent variables from DATA1 computed for observations

ly

latent variables from DATA2 computed for observations

Author(s)

Derek Beaton, Hervé Abdi

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.
Abdi, H. (2007). Discriminant correspondence analysis. In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage. pp. 270-275.
Pinkham, A.E., Sasson, N.J., Beaton, D., Abdi, H., Kohler, C.G., Penn, D.L. (in press, 2012). Qualitatively distinct factors contribute to elevated rates of paranoia in autism and schizophrenia. Journal of Abnormal Psychology, 121, -.
Williams, L.J., Abdi, H., French, R., & Orange, J.B. (2010). A tutorial on Multi-Block Discriminant Correspondence Analysis (MUDICA): A new method for analyzing discourse data from clinical populations. Journal of Speech Language and Hearing Research, 53, 1372-1393.
Williams, L.J., Dunlop, J.P., & Abdi, H. (2012). Effect of age on the variability in the production of text-based global inferences. PLoS One, 7(5): e36161. doi:10.1371/ journal.pone.0036161 (pp.1-9)

See Also

coreCA, epCA, epMCA
For MatLab code: http://utd.edu/~herve/HerveAbdi_MatlabPrograms4MUDICA.zip For additional R code (with inference tests): http://utdallas.edu/~dfb090020/attachments/MuDiCA.zip

Examples

data(dica.wine)
dica.res <- tepDICA(dica.wine$data,DESIGN=dica.wine$design,make_design_nominal=FALSE)