TExPosition (version 2.9.0)

tepBADA: Barycentric Discriminant Analysis

Description

Barycentric Discriminant Analysis (BADA) via TExPosition.

Usage

tepBADA(
  DATA,
  scale = TRUE,
  center = TRUE,
  DESIGN = NULL,
  make_design_nominal = TRUE,
  graphs = TRUE,
  k = 0
)

Value

See 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

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.

graphs

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

k

number of components to return.

Author

Derek Beaton

Details

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

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, epMDS

Examples

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