This is for Brett Human - I have tried to respond to your latest posting but the address you give is bouncing.
Rob Kidd At: [EMAIL PROTECTED] -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Sent: Wednesday, 26 May 2004 9:08 AM To: [EMAIL PROTECTED] Subject: Re: size correction & discriminant functions analyses G'day all, Sender: [EMAIL PROTECTED] Precedence: bulk Reply-To: [EMAIL PROTECTED] Thanks to everyone for your comments. They've been a great help, and I'm glad that my question sparked a bit of discussion on the subject. After some pondering, I've got a few more questions and some more details on the way I analysed my data. Although I was looking for species clustering, I wasn't terribly concerned with quantifying any clustering, and was using PCA more as a visualisation technique to explore my data. In the future I will try the various methods suggested to try to quantify the clustering. Another thing was with regards to the issue of multivariate normality. I did not use a variance-covariance matrix, instead I used a correlation matrix. I was under the assumption that by transforming the covariances into z-scores, I would have a greater chance of my data being (or approaching) multivariate normality? Also, for testing if my data is normally distributed, if I was to do separate PCA's for each population and if a population was normally dist., then would I expect to see an ellipsoid with it's greatest length along PC1 in a PCA plot? With regards to obtaining singular matrices when # measures >> # specimens, this did happen to me and the way I 'got round' this was to first regress every measurement against total length and then by looking at the slopes of the regressions, chose which measurements showed the greatest potential for between species differentiation. Because I was using PCA just as a qualitative tool, I didn't think it was much of a problem, however if I want to do quantitative analysis such as discriminant analysis, can I still use this same method of choosing measures, or am I restricted to stepwise methods using the whole data set? Forgive my ignorance, but what is NMDS and CVA? I assume PCO is principal coordinates analysis? I would also appreciate a pdf of the Darroch & Mosimann paper if available. A final point, to perhaps spark more debate or at least to motivate some thought, is that I have found it very difficult to get a basic understanding of the application of multivariate stats to morphometrics because the text books available are very technical. An equation may be meaningful to the gurus, but it doesn't mean a whole lot to me. It is also one thing to describe how a procedure works, but it's another thing to implement it when you are ignorant of the software availble. I think there is a great need for a text book that can introduce the new student to this field without using equations to describe what's going on. There - I've said it, let the slaughter begin. Thanks, Brett ***************************** Brett Human Shark Researcher 27 Southern Ave West Beach SA 5024 Australia +61 8 8356 6891 [EMAIL PROTECTED] ***************************** == Replies will be sent to list. For more information see http://life.bio.sunysb.edu/morph/morphmet.html. == Replies will be sent to list. For more information see http://life.bio.sunysb.edu/morph/morphmet.html.
