Anders; If you want to _test_ for differences, ANOVA applied to on the (typically) first principal component scores for each object would give a fairly quick indication of whether there was a case to answer (though scaling is an issue to be aware of; a low-variance variable might differ strongly between groups yet be masked by a larger variance variable wiht no group association unless you get the scaling right for the circumstances).
If you just want to cluster the 10 groups, I suspect it might be simplest to "average" (where "average" implies some consistent summary statistic for each variable) your starting vectors, _before_ playing about with your distance matrix; after all, it is the inter-"mean" distances you are after, so why not get the "means" in the first place?. Of course, scaling is again an issue if the variables differ in variance... Steve E >>> Anders Malmendal <[EMAIL PROTECTED]> 29/05/2007 10:15:23 >>> I want to do hierarchical cluster analysis to compare 10 groups of vectors with five vectors in each group (i.e. I want to make a dendogram showing the clustering of the different groups). I've looked into using dist and hclust, but cannot see how to compare the different groups instead of the individual vectors. I am thankful for any help. Anders ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. ******************************************************************* This email and any attachments are confidential. Any use, co...{{dropped}} ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
