That's not really true. Lyn Hunt's program Multimix hybridizes between mixtures of multivariate normals and latent class analysis and copes with both continuous and categorical variables. Covariance matrices are estimated separately within clusters. It does help if the structure of these within cluster covariance matrices can be specified as block-diagonal matrices as this reduces the number of parameters and stabilizes the estimation. Missing values in variables may be coped with.
Murray Jorgensen At 06:32 23/10/02 -0500, shannon wrote: >Hi > >I would think this could occur only in a special case where a mixture >model approach can be used. The data would need to be from three different >multivariate normal distributions, each with the same covariance matrix. >If you do a web search on 'mixture models' you will come up with the >information you need. > >I don't know of and can't imagine any type of hierarchical or scaling >approach that could be used. > > >Bill >--- > >William D. Shannon, Ph.D. > >Assistant Professor of Biostatistics in Medicine >Division of General Medical Sciences and Biostatistics > >Washington University School of Medicine >Campus Box 8005, 660 S. Euclid >St. Louis, MO 63110 > >Phone: 314-454-8356 >Fax: 314-454-5113 >e-mail: [EMAIL PROTECTED] >web page: http://ilya.wustl.edu/~shannon > > >On Wed, 23 Oct 2002, Marinucci, Max (MB Ergo) wrote: > >> Dear all >> >> >> I would like to know if there is some clustering provedure which does the >> following.Given a data set with n observations on k variables with >> correlations matrix R (k x k) I would like to obtain 3 cluster of >> approximatively equal size n1=n2=n3 that satisfy the following condition. >> >> >> The correlations matrix of each of the three subgroups should be as close as >> possible each other and with respect to the pooled correlation matrix, That >> is R1=R2=R3=R >> >> >> Do you have any suggestions or ideas on how to proceed to obtain such >> partitions? >> >> >> Thanx a lot >> >> >> Massimiliano Marinucci >> >> >> Phd candidate >> >> >> Universidad Complutense Madrid >> >> >> >> >> >> >> ==================================================== >> This email is confidential and intended solely for the use of the >> individual or organisation to whom it is addressed. Any opinions or >> advice presented are solely those of the author and do not necessarily >> represent those of the Millward Brown Group of Companies. If you are >> not the intended recipient of this email, you should not copy, modify, >> distribute or take any action in reliance on it. If you have received >> this email in error please notify the sender and delete this email >> from your system. Although this email has been checked for viruses >> and other defects, no responsibility can be accepted for any loss or >> damage arising from its receipt or use. >> ==================================================== >> > Dr Murray Jorgensen http://www.stats.waikato.ac.nz/Staff/maj.html Department of Statistics, University of Waikato, Hamilton, New Zealand Email: [EMAIL PROTECTED] Fax +64-7 838 4155 Phone +64-7 838 4773 wk +64 7 849 6486 home Mobile 021 395 862
