Brett- I'm not sure just what analysis you are carrying out, but one alternative approach is to use a PCA to reduce the dimensionality of your data prior to further analysis. One might discard some number of the PCA axes, retaining enough to explain 95% of the variance in your data.
This is a loss of information of course, which may not be acceptable in your analysis. -Dave ---- Original message ---- >Date: Wed, 16 Mar 2005 09:27:19 -0500 >From: morphmet <[EMAIL PROTECTED]> >Subject: generalised inverse matrices >To: 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
