On Tue, 29 May 2007, Anders Malmendal wrote: > Thanks. > The vectors are produced by PLS-discriminant analysis between groups and > the vectors within a group are simply different measurements of the same > thing. What I need is a measure of how the different groups cluster > (relative to each other). (I assume that I can do some averaging after > applying dist, but I can not find the information on how to do it.)
I don't think anyone can tell you that: it is a matter of judgement. What you need is a dissimilarity on your groups. Assuming your vectors are numeric (you didn't say) you could use Mahalanobis distance between the centroids, with within-group covariance as the variance matrix. Often that works well, but not always, and you might prefer Euclidean distance between centroids, or minimum Euclidean or Mahalanobis distance .... > Best regards > Anders > > > Rafael Duarte wrote: >> It seems that you have already groups defined. >> Discriminant analysis would probably be more appropriate for what you >> want. >> Best regards, >> Rafael Duarte >> >> >> >> Anders Malmendal wrote: >> >>> 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 >>> >>> ______________________________________________ >>> R-help@stat.math.ethz.ch 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. >>> >>> >> >> > > > -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ R-help@stat.math.ethz.ch 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.
