cc {CCA}

Canonical Correlation Analysis

Description

The function performs Canonical Correlation Analysis to highlight correlations between two data matrices. It complete the cancor() function with supplemental numerical and graphical outputs and can handle missing values.

Usage

cc(X, Y)

Arguments

X

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

Y

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

Details

The canonical correlation analysis seeks linear combinations of the 'X' variables which are the most correlated with linear combinations of the 'Y' variables.

Let PX and PY be the projector onto the respective column-space of X and Y. The eigenanalysis of PXPY provide the canonical correlations (square roots of the eigenvalues) and the coefficients of linear combinations that define the canonical variates (eigen vectors).

Value

A list containing the following components:

cor

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

www.lsp.ups-tlse.fr/CCA

See Also

rcc, plt.cc

Examples

data(nutrimouse)
X=as.matrix(nutrimouse$gene[,1:10])
Y=as.matrix(nutrimouse$lipid)
res.cc=cc(X,Y)
plot(res.cc$cor,type="b")
plt.cc(res.cc)