Tests the sequential hypotheses that the \(i\)th canonical correlation and all that follow it are zero, $$\rho_i = \rho_{i+1} = \cdots = 0$$
Usage
Wilks(object, ...)
# S3 method for cancor
Wilks(object, ...)
# S3 method for candisc
Wilks(object, ...)Arguments
- object
An object of class
"cancor""} or \code{"candisc""- ...
Other arguments passed to methods (not used)
Details
Wilks' Lambda values are calculated from the eigenvalues and converted to F statistics using Rao's approximation.
Methods (by class)
Wilks(cancor):"cancor"method.Wilks(candisc):print()method for"candisc"objects.
References
Mardia, K. V., Kent, J. T. and Bibby, J. M. (1979). Multivariate Analysis. London: Academic Press.
See also
cancor, ~~~
Examples
data(Rohwer, package="heplots")
X <- as.matrix(Rohwer[,6:10]) # the PA tests
Y <- as.matrix(Rohwer[,3:5]) # the aptitude/ability variables
cc <- cancor(X, Y, set.names=c("PA", "Ability"))
Wilks(cc)
#>
#> Test of H0: The canonical correlations in the
#> current row and all that follow are zero
#>
#> CanR LR test stat approx F numDF denDF Pr(> F)
#> 1 0.67033 0.44011 3.8961 15 168.8 5.535e-06 ***
#> 2 0.38366 0.79923 1.8379 8 124.0 0.07608 .
#> 3 0.25065 0.93718 1.4078 3 63.0 0.24881
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
iris.mod <- lm(cbind(Petal.Length, Sepal.Length, Petal.Width, Sepal.Width) ~ Species, data=iris)
iris.can <- candisc(iris.mod, data=iris)
Wilks(iris.can)
#>
#> Test of H0: The canonical correlations in the
#> current row and all that follow are zero
#>
#> LR test stat approx F numDF denDF Pr(> F)
#> 1 0.02344 199.145 8 288 < 2.2e-16 ***
#> 2 0.77797 13.794 3 145 5.794e-08 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1