rcca_da: Regularized canonical correlation analysis for discriminant...

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

Regularized canonical correlation analysis for discriminant analysis (RCCA-DA)

Description

This function performs regularized canonical correlation analysis for discriminant analysis (RCCA-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

rcca_da(X,Y,lambda,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.
lambda	The regularized parameter has a value in the range [0, 1), meaning it can be 0 but is less than 1."
k	Number of components.

Details

RCCA-DA is equivalent to Regularized Fisher discriminant analysis, theoretically.

Value

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

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

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

Author(s)

Hiroyuki Yamamoto

References

Yamamoto, H. et al., Canonical correlation analysis for multivariate regression and its application to metabolic fingerprinting", Biochem. Eng. Journal, 40 (2008) 199-204.

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

rcca <- rcca_da(X,Y,0.5,2)

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