loadings (version 0.5.1)

rcca_da: 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)

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

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.

Author

Hiroyuki Yamamoto

Details

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

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)