TExPosition (version 2.9.0)

tepDICA: Discriminant Correspondence Analysis

Description

Discriminant Correspondence Analysis (DICA) via TExPosition.

Usage

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

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

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.

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.

Author

Derek Beaton, Hervé Abdi

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.

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

Examples

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