Regularized Canonical Correlation Analysis
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)
rcc(X, Y, lambda1, lambda2)
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 |
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.
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 |
ycoef |
estimated coefficients for the 'Y' variables as returned by |
scores |
a list returned by the internal function comput() containing individuals and variables coordinates on the canonical variates basis. |
Sébastien Déjean, Ignacio González
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.
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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