R: Canonical correlation analysis

canocor {calibrate} | R Documentation

## 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

### 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 jan.graffelman@upc.edu

### 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)

[Package calibrate version 1.7.2 Index]