rcca_da {loadings} | R Documentation |
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)