calibrate (version 1.7.7)

rda: Redundancy analysis

Description

rda performs redundancy analysis and stores extensive output in a list object.

Usage

rda(X, Y, scaling = 1)

Arguments

X

a matrix of x variables

Y

a matrix of y variables

scaling

scaling used for x and y variables. 0: x and y only centered. 1: x and y standardized

Value

Returns a list with the following results

Yh

fitted values of the regression of y on x

B

regression coefficients of the regresson of y on x

decom

variance decomposition/goodness of fit of the fitted values AND of the regression coefficients

Fs

biplot markers of the rows of Yh (standard coordinates)

Fp

biplot markers of the rows of Yh (principal coordinates)

Gys

biplot markers for the y variables (standard coordinates)

Gyp

biplot markers for the y variables (principal coordinates)

Gxs

biplot markers for the x variables (standard coordinates)

Gxp

biplot markers for the x variables (principal coordinates)

Details

Results are computed by doing a principal component analyis of the fitted values of the regression of y on x.

Plotting the first two columns of Gxs and Gyp, or of Gxp and Gys provides a biplots of the matrix of regression coefficients.

Plotting the first two columns of Fs and Gp or of Fp and Gs provides a biplot of the matrix of fitted values.

References

Van den Wollenberg, A.L. (1977) Redundancy Analysis, an alternative for canonical correlation analysis. Psychometrika 42(2): pp. 207-219.

Ter Braak, C. J. F. and Looman, C. W. N. (1994) Biplots in Reduced-Rank Regression. Biometrical Journal 36(8): pp. 983-1003.

See Also

princomp,canocor,biplot

Examples

# NOT RUN {
X <- matrix(rnorm(75),ncol=3)
Y <- matrix(rnorm(75),ncol=3)
rda.results <- rda(X,Y)
# }