rcc: 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

numeric matrix (n * p), containing the X coordinates.

numeric matrix (n * q), containing the Y coordinates.

lambda1

Regularization parameter for X

lambda2

Regularization parameter for Y

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.

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.

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.

Examples

Run this code

# NOT RUN {
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)
# }

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