pls_rog: PLS-ROG : Partial least squares with rank order of groups

In loadings: Loadings for Principal Component Analysis and Partial Least Squares

PLS-ROG : Partial least squares with rank order of groups

Description

This function performs partial least squares with rank order of groups (PLS-ROG). In this function, data matrix is automatically scaled to zero mean and unit variance (i.e. autoscaling) for each variables.

Usage

pls_rog(X,Y,D,kappa)

Arguments

X	Data matrix of explanatory variables that include variables in each columns.
Y	Dummy matrix that include group information 0,1 in each columns.
D	Differential matrix.
kappa	The smoothing parameter (default : kappa = 0.999).

Details

The kappa represents the degree of smoothing. The value of kappa increases, the strength of the smoothing increases.

Value

The return value is a list object that contains the following elements:

P : A matrix with PLS loading for explanatory variable in each column

T : A matrix with PLS score for explanatory variable in each column

Q : A matrix with PLS loading for response variable in each column

U : A matrix with PLS score for response variable in each column

Author(s)

Hiroyuki Yamamoto

References

Yamamoto, H. (2017) PLS-ROG: Partial least squares with rank order of groups., Journal of Chemometrics, 31(3) (2017) e2883.

Examples

data(whhl)
X <- whhl$X$liver
Y <- whhl$Y
D <- whhl$D

plsrog <- pls_rog(X,Y,D)

loadings documentation built on May 29, 2024, 8:01 a.m.