Hello Brett! Generalized inverses are quite kosher. In fact, they are used widely in multivariate analysis of which geometric morphometrics forms a part. There is a standard text by R. K. Rao and a coworker from 1962. I do not have the reference at hand but can easily get it for you since I have the book.
Norm Campbell of the CSIRO Division of Maths and stats at Wembley is a mine of information (Norm has taken early retirement but is still a consultant with the DMS). Richard A. Reyment Sharks! I'd certainly love to have a nice serving of flake and potato scallops. (ie gummy shark - I see from the Oz Nat. Dictionary that flake is not listed and hence must be Melbourne dialect for Mustelus antarcticus). Citerar morphmet <[EMAIL PROTECTED]>: > Hello All, > > I am having issues with singular matrices because the number of variables > (linear morphometric measures) I have far exceeds the number of samples. I > don't wish to introduce a bias into my analyses by selecting variables to > include/exclude from my data matrix, hence I am wondering about the validity > of using generalised inverse matrices. > > Is the use of generalised inverse matrices a valid/ accepted one? > Are generalised inverse matrices statistically robust and will people > believe my results? > Has anyone published using generalised inverse matrices? > How would I defend the use of generalised inverse matrices? > > Thanks in advance, > > Brett Human > > ************************ > Brett Human, PhD > Shark Researcher > 27 Southern Ave > West Beach SA 5024 > Australia > Ph: +61 8 8356 6891 > email: [EMAIL PROTECTED] > ************************ > -- > Replies will be sent to the list. > For more information visit http://www.morphometrics.org > -- Replies will be sent to the list. For more information visit http://www.morphometrics.org
