pls_da: Partial least squares discriminant analysis (PLS-DA)

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

Partial least squares discriminant analysis (PLS-DA)

Description

This function performs partial least squares discriminant analysis (PLS-DA). In this function, data matrix for explanatory variable is automatically scaled to zero mean and unit variance (i.e. autoscaling) for each variables.

Usage

pls_da(X,Y,k)

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.
k	Number of components.

Details

This function calculates PLS-DA. For PLS, use the 'pls_svd' function for PLS.

Value

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

P: A matrix containing the PLS-DA loadings for each explanatory variable in the columns, before transformation.

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

Author(s)

Hiroyuki Yamamoto

References

Yamamoto, H. et al., Dimensionality reduction for metabolome data using PCA, PLS, OPLS, and RFDA with differential penalties to latent variables", Chemom. Intell. Lab. Syst., 98 (2009)

Examples

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

plsda <- pls_da(X,Y,2)

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