ade4 (version 1.7-19)

testdim: Function to perform a test of dimensionality

Description

This functions allow to test for the number of axes in multivariate analysis. The procedure testdim.pca implements a method for principal component analysis on correlation matrix. The procedure is based on the computation of the RV coefficient.

Usage

testdim(object, ...)
# S3 method for pca
testdim(object, nrepet = 99, nbax = object$rank, alpha = 0.05, ...)

Value

An object of the class krandtest. It contains also:

nb

The estimated number of axes to keep

nb.cor

The number of axes to keep estimated using a sequential Bonferroni procedure

Arguments

object

an object corresponding to an analysis (e.g. duality diagram, an object of class dudi)

nrepet

the number of repetitions for the permutation procedure

nbax

the number of axes to be tested, by default all axes

alpha

the significance level

...

other arguments

Author

Stéphane Dray

References

Dray, S. (2008) On the number of principal components: A test of dimensionality based on measurements of similarity between matrices. Computational Statistics and Data Analysis, Volume 52, 2228--2237. doi:10.1016/j.csda.2007.07.015

See Also

dudi.pca, RV.rtest,testdim.multiblock

Examples

tab <- data.frame(matrix(rnorm(200),20,10))
pca1 <- dudi.pca(tab,scannf=FALSE)
test1 <- testdim(pca1)
test1
test1$nb
test1$nb.cor
data(doubs)
pca2 <- dudi.pca(doubs$env,scannf=FALSE)
test2 <- testdim(pca2)
test2
test2$nb
test2$nb.cor