tepPLS {TExPosition}

Partial Least Squares

Description

Partial Least Squares (PLS) via TExPosition.

Usage

tepPLS(DATA1, DATA2,
center1 = TRUE, scale1 = "SS1", center2 = TRUE, scale2 = "SS1",
DESIGN = NULL, make_design_nominal = TRUE,
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.

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).

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)
McIntosh, A. R., Bookstein, F. L., Haxby, J. V., & Grady, C. L. (1996). Spatial Pattern Analysis of Functional Brain Images Using Partial Least Squares. NeuroImage, 3(3), 143–157.

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, tepBADA, tepGPLS, tepPLSCA

Examples

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