Calculates indices of redundancy (Stewart & Love, 1968) from a canonical correlation analysis. These give the proportion of variances of the variables in each set (X and Y) which are accounted for by the variables in the other set through the canonical variates.
redundancy(object,...)## S3 method for class 'cancor.redundancy'print(x, digits = max(getOption("digits")-3,3),...)
Arguments
object: A "cancor" object
...: Other arguments
x: A "cancor.redundancy" for the print method.
digits: Number of digits to print
Returns
An object of class "cancor.redundancy", a list with the following 5 components: - Xcan.redun: Canonical redundancies for the X variables, i.e., the total fraction of X variance accounted for by the Y variables through each canonical variate.
Ycan.redun: Canonical redundancies for the Y variables
X.redun: Total canonical redundancy for the X variables, i.e., the sum of Xcan.redun.
Y.redun: Total canonical redundancy for the Y variables
set.names: names for the X and Y sets of variables
Details
The term "redundancy analysis" has a different interpretation and implementation in the environmental ecology literature, such as the vegan. In that context, each Yi variable is regressed separately on the predictors in X, to give fitted values Y=[Y1,Y2,…. Then a PCA of Y is carried out to determine a reduced-rank structure of the predictions.
Functions
print(cancor.redundancy): print() method for "cancor.redundancy" objects.
Examples
data(Rohwer, package="heplots")X <- as.matrix(Rohwer[,6:10])# the PA testsY <- as.matrix(Rohwer[,3:5])# the aptitude/ability variablescc <- cancor(X, Y, set.names=c("PA","Ability"))redundancy(cc)## ## Redundancies for the PA variables & total X canonical redundancy## ## Xcan1 Xcan2 Xcan3 total X|Y ## 0.17342 0.04211 0.00797 0.22350 ## ## Redundancies for the Ability variables & total Y canonical redundancy## ## Ycan1 Ycan2 Ycan3 total Y|X ## 0.2249 0.0369 0.0156 0.2774
References
Muller K. E. (1981). Relationships between redundancy analysis, canonical correlation, and multivariate regression. Psychometrika, 46 (2), 139-42.
Stewart, D. and Love, W. (1968). A general canonical correlation index. Psychological Bulletin, 70, 160-163.