rcc: Regularized Canonical Correlation Analysis

In CCA: Canonical Correlation Analysis

Regularized Canonical Correlation Analysis

Description

The function performs the Regularized extension of the Canonical Correlation Analysis to seek correlations between two data matrices when the number of columns (variables) exceeds the number of rows (observations)

Usage

rcc(X, Y, lambda1, lambda2)

Arguments

X	numeric matrix (n * p), containing the X coordinates.
Y	numeric matrix (n * q), containing the Y coordinates.
lambda1	Regularization parameter for X
lambda2	Regularization parameter for Y

Details

When the number of columns is greater than the number of rows, the matrice X'X (and/or Y'Y) may be ill-conditioned. The regularization allows the inversion by adding a term on the diagonal.

Value

A list containing the following components:

corr	canonical correlations
names	a list containing the names to be used for individuals and variables for graphical outputs
xcoef	estimated coefficients for the 'X' variables as returned by cancor()
ycoef	estimated coefficients for the 'Y' variables as returned by cancor()
scores	a list returned by the internal function comput() containing individuals and variables coordinates on the canonical variates basis.

Author(s)

Sébastien Déjean, Ignacio González

References

Leurgans, Moyeed and Silverman, (1993). Canonical correlation analysis when the data are curves. J. Roy. Statist. Soc. Ser. B. 55, 725-740.

Vinod (1976). Canonical ridge and econometrics of joint production. J. Econometr. 6, 129-137.

See Also

cc, estim.regul, plt.cc

Examples

data(nutrimouse)
X=as.matrix(nutrimouse$gene)
Y=as.matrix(nutrimouse$lipid)
res.cc=rcc(X,Y,0.1,0.2)
plt.cc(res.cc)

CCA documentation built on Sept. 8, 2023, 5:19 p.m.