calibrate (version 1.7.7)

canocor: Canonical correlation analysis

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

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

Details

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

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

# NOT RUN {
set.seed(123)
X <- matrix(runif(75),ncol=3)
Y <- matrix(runif(75),ncol=3)
cca.results <- canocor(X,Y)
# }