tepGPLS {TExPosition}

Generalized Partial Least Squares

Description

Generalized Partial Least Squares (GPLS) via TExPosition. GPLS is to PLS (tepPLS) as PCA epPCA is to GPCA epGPCA. The major difference between PLS and GPLS is that GPLS allows the use of weights for the columns of each data set (just like GPCA).

Usage

tepGPLS(DATA1, DATA2, 
center1 = TRUE, scale1 = "SS1",
center2 = TRUE, scale2 = "SS1",
DESIGN = NULL, make_design_nominal = TRUE,
weights1 = NULL, weights2 = NULL,
graphs = TRUE, k = 0)

Arguments

DATA1

Data matrix 1 (X)

DATA2

Data matrix 2 (Y)

center1

a boolean, vector, or string to center DATA1. See expo.scale for details.

scale1

a boolean, vector, or string to scale DATA1. See expo.scale for details.

center2

a boolean, vector, or string to center DATA2. See expo.scale for details.

scale2

a boolean, vector, or string to scale DATA2. See expo.scale for details.

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.

weights1

a weight vector (or diag matrix) for the columns of DATA1.

weights2

a weight vector (or diag matrix) for the columns of DATA2.

graphs

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

k

number of components to return.

Details

This implementation of Partial Least Squares is a symmetric analysis. It was first described by Tucker (1958), again by Bookstein (1994), and has gained notoriety in Neuroimaging from McIntosh et al., (1996). This particular implementation allows the user to provide weights for the columns of both DATA1 and DATA2.

Value

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

lx

latent variables from DATA1 computed for observations

ly

latent variables from DATA2 computed for observations

data1.norm

center and scale information for DATA1

data1.norm

center and scale information for DATA2

Author(s)

Derek Beaton

References

Tucker, L. R. (1958). An inter-battery method of factor analysis. Psychometrika, 23(2), 111–136.
Bookstein, F., (1994). Partial least squares: a dose–response model for measurement in the behavioral and brain sciences. Psycoloquy 5 (23)
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.
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.
McIntosh, A. R., & Lobaugh, N. J. (2004). Partial least squares analysis of neuroimaging data: applications and advances. Neuroimage, 23, S250–S263.

See Also

corePCA, epPCA, epGPCA, tepPLS, tepPLSCA, tepBADA, tepDICA

Examples

data(beer.tasting.notes)
data1<-beer.tasting.notes$data[,1:8]
data2<-beer.tasting.notes$data[,9:16]
gpls.res <- tepGPLS(data1,data2)