Dear R users, I m quite a novice in using R for factor analysis and I would need some help to choose the right function. I have a contingency table and I would like to perform a Correspondence analysis on this table, followed by a hirarchical clustering of my variables projected in on the first principal components.
Here are my question : - what is the more appropriate function to do so ... I already tried by using the function 'corresp' in the MASS package - it seems to work ... but how is it possible to get all the information concerning the non-centered PCA used by corresp (eigen-values, inertia, scree plot, square cos, ....) In a second step I would like to use hirarchical clustering from the results of the correspondence analysis ... If "Table" is my contingency table (i.e., the number of individuals seen in each case) ... I tried to implment it as follow (for the 2 first components for instance, but I would be interesting at looking to the other components ...I did not manage to get the eigen values !) : A <- corresp(Table, nf = 2) biplot(A) hc <- hclust(dist(A$cscore), "ward") plot(hc) Is that ok ??? ... here it is exemplae using Ward method for clustering ... I would also be interested in using method of mutual neighbors to identify the clusters ... is it possible using hclust ? Any help would be really appreciated, Best regards ... Christophe *************************** Christophe Grova, PhD PostDoc - EEG department Montreal Neurological Institute, McGill University 3801 University Street, Montreal, Quebec, Canada, H3A 2B4 email : [EMAIL PROTECTED] tel : (514) 398 2184 fax : (514) 398 8106 web: http://idm.univ-rennes1.fr/users/grova *************************** [[alternative HTML version deleted]] ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
