canocor: Canonical correlation analysis

In calibrate: Calibration of Scatterplot and Biplot Axes

Description

canocor performs canonical correlation analysis on the basis of the standardized variables and stores extensive output in a list object.

Usage

canocor(X, Y)

Arguments

X	a matrix containing the X variables
Y	a matrix containing the Y variables

Details

canocor computes the solution by a singular value decomposition of the transformed between set correlation matrix.

Value

Returns a list with the following results

ccor	the canonical correlations
A	canonical weights of the x variables
B	canonical weights of the y variables
U	canonical x variates
V	canonical y variates
Fs	biplot markers for x variables (standard coordinates)
Gs	biplot markers for y variables (standard coordinates)
Fp	biplot markers for x variables (principal coordinates)
Gp	biplot markers for y variables (principal coordinates)
fitRxy	goodness of fit of the between-set correlation matrix
fitXs	adequacy coefficients of x variables
fitXp	redundancy coefficients of x variables
fitYs	adequacy coefficients of y variables
fitYp	redundancy coefficients of y variables

Author(s)

Jan Graffelman [e-mail]

References

Hotelling, H. (1935) The most predictable criterion. Journal of Educational Psychology (26) pp. 139-142.

Hotelling, H. (1936) Relations between two sets of variates. Biometrika (28) pp. 321-377.

Johnson, R. A. and Wichern, D. W. (2002) Applied Multivariate Statistical Analysis. New Jersey: Prentice Hall.

See Also

cancor

Examples

set.seed(123)
X <- matrix(runif(75),ncol=3)
Y <- matrix(runif(75),ncol=3)
cca.results <- canocor(X,Y)

Example output

Loading required package: MASS

calibrate documentation built on July 1, 2020, 7:03 p.m.