| testdim {ade4} | R Documentation |
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.
testdim(object, ...) ## S3 method for class 'pca' testdim(object, nrepet = 99, nbax = object$rank, alpha = 0.05, ...)
object |
an object corresponding to an analysis (e.g. duality diagram, an object of class |
nrepet |
the number of repetitions for the permutation procedure |
nbax |
the number of axes to be tested, by default all axes |
alpha |
the significance level |
... |
other arguments |
An object of the class krandtest. It contains also:
nb |
The estimated number of axes to keep |
nb.cor |
The number of axes to keep estimated using a sequential Bonferroni procedure |
Stephane Dray stephane.dray@univ-lyon1.fr
Dray, S. (2008) On the number of principal components: A test of dimensionality based on measurements of similarity between matrices. Computational Statistics and Data Analysis, Volume 52, 2228–2237. doi:10.1016/j.csda.2007.07.015
dudi.pca, RV.rtest,testdim.multiblock
tab <- data.frame(matrix(rnorm(200),20,10)) pca1 <- dudi.pca(tab,scannf=FALSE) test1 <- testdim(pca1) test1 test1$nb test1$nb.cor data(doubs) pca2 <- dudi.pca(doubs$env,scannf=FALSE) test2 <- testdim(pca2) test2 test2$nb test2$nb.cor