tepBADA {TExPosition}

Barycentric Discriminant Analysis

Description

Barycentric Discriminant Analysis (BADA) via TExPosition.

Usage

tepBADA(DATA, scale = TRUE, center = TRUE, DESIGN = NULL, make_design_nominal = TRUE, 
	group.masses = NULL, weights = NULL, graphs = TRUE, k = 0)

Arguments

DATA

original data to perform a BADA on.

scale

a boolean, vector, or string. See expo.scale for details.

center

a boolean, vector, or string. See expo.scale for details.

DESIGN

a design matrix to indicate if rows belong to groups. Required for BADA.

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 items.

graphs

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

k

number of components to return.

Details

Note: BADA is a special case of PLS (tepPLS,tepGPLS) wherein DATA1 are data and DATA2 are a group-coded disjunctive matrix. This is also called mean-centered PLS (Krishnan et al., 2011).

Value

See epGPCA (and also corePCA) 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

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. & Williams, L.J. (2010). Barycentric discriminant analysis (BADIA). In N.J. Salkind, D.M., Dougherty, & B. Frey (Eds.): Encyclopedia of Research Design. Thousand Oaks (CA): Sage. pp. 64-75.
Abdi, H., Williams, L.J., Beaton, D., Posamentier, M., Harris, T.S., Krishnan, A., & Devous, M.D. (in press, 2012). Analysis of regional cerebral blood flow data to discriminate among Alzheimer's disease, fronto-temporal dementia, and elderly controls: A multi-block barycentric discriminant analysis (MUBADA) methodology. Journal of Alzheimer Disease, , -. Abdi, H., Williams, L.J., Connolly, A.C., Gobbini, M.I., Dunlop, J.P., & Haxby, J.V. (2012). Multiple Subject Barycentric Discriminant Analysis (MUSUBADA): How to assign scans to categories without using spatial normalization. Computational and Mathematical Methods in Medicine, 2012, 1-15. doi:10.1155/2012/634165.
Krishnan, A., Williams, L. J., McIntosh, A. R., & Abdi, H. (2011). Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review. NeuroImage, 56(2), 455 – 475.

See Also

corePCA, epPCA, epGPCA, epMDS
For MatLab code: http://utd.edu/~derekbeaton/attachments/Software/matlab/MuSuBADA_V3.zip

Examples

data(bada.wine)
bada.res <- tepBADA(bada.wine$data,scale=FALSE,DESIGN=bada.wine$design,make_design_nominal=FALSE)