Andy: See e.g. the pls package. However, be forewarned: this is a vague problem (what kind of predictors/responses do you want? -- linear combinations? nonlinear combinations? ...). The problem is also NP-Hard I believe, so solutions are very algorithm (and even starting value)-dependent. For these reasons, statistical inference is difficult, at best, and probably not even meaningful in your context, as I doubt that you have a random sample of anything. A personal recommendation (with which many disagree, I know): seek extreme parsimony in both predictors and responses for results to be replicable/scientifically meaningful.
Bert Gunter Genentech Nonclinical Statistics -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Andy Weller Sent: Monday, July 02, 2007 8:17 AM To: R-help@stat.math.ethz.ch Subject: [R] Salient feature selection I am relatively new to R. I am hoping that someone will be able to point me in the right direction and/or suggest a technique/package/reference that will help me with the following. I have: a) Some explanatory variables (integers, real) - these are "real world" physical descriptions, i.e. counts of features, etc b) Some response variables (integers, real) - these are image analysis measurements (gray-value distributions, textural descriptors, etc) of the same things represented in a and I want to find out which between the two correlate best - i.e. the salient features from BOTH sets (i.e. not for classification purposes). For example, if a has 10 explanatory variables and b has 10 response variables, I want to test the complete set of explanatory variables with each individual response (or vice versa). So, explanatory 1-10 with response 1, explanatory 1-10 with response 2, explanatory 1-10 with response 3, etc... This should ultimately tell me which "real world" physical features are related best with the image analysis measurements (with the confidence level between them). I hope this makes sense? I have used SPSS AnswerTree's "Exhaustive CHAID" before to select a subset of input features for a complete set of output features to aid the creation of artificial neural networks. I want to do a similar thing, but it is not important for ALL explanatory and response variables are used/selected. I hope that I have been clear in my intentions and I look forward to your replies, Andy ______________________________________________ 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. ______________________________________________ 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.