Hi Gareth, >> My data is transformed to the clr or alr under Aitchison geometry, so I >> am essentially working >> in Euclidean space.
Great: glad to hear it. >> Has anyone had experience doing stepwise LDA?? I can't for the life of >> me find any help >> online about where to start. A better option might be this: Trevor Hastie and a student of his have recently put out a paper that does a step-up from penalized discriminant analysis based, I think, on Trevor's sparse principal component analysis method (in his elasticnet package). http://www-stat.stanford.edu/~hastie/Papers/sda_line.pdf You can get R-code to do the analysis on the first author's website; there's a link in the paper. Bye, Mark. gcam032 wrote: > > Thanks Mark, > > I failed to mention that i'm working within a compositional framework. I > didn't want to confuse things. My data is transformed to the clr or alr > under Aitchison geometry, so I am essentially working in Euclidean space. > > Has anyone had experience doing stepwise LDA?? I can't for the life of me > find any help online about where to start. > > Thanks > > Gareth > > > quote author="Mark Difford"> > Hi Gareth, > >>> If I use the full composition (31 elements or variables), I can get >>> reasonable separation of my 6 sources. > > A word of advice: You need to be exceptionally careful when analyzing > compositional data. Taking compositions puts your data values into a > constrained/bounded space (generally called a simplex) so that most > standard statistical procedures (i.e. anything that uses a Euclidean > metric, and most do) deliver erroneous results. Pearson wrote a paper on > this long ago, but it's generally been ignored (except by Aitchison and > the Spanish School of mathematical statisticians). > > The problem is comparatively well known to geologists, who work with > compositional much of the time. R has a very good package for analysing > this data-type: see the compositions package (a new release seems > iminent). You will be able to get most of the main references from it. > (The authors of the package also have a newly-released article in one of > the Elsevier journals [unfor. my bib+ are elsewhere so I cannot give > details]). > > You could start by Wiki'ing your way to "compositional data". > > HTH, Mark. > > > > Gareth Campbell wrote: >> >> Hello all, >> >> I'm dealing with geochemical analyses of some rocks. >> >> If I use the full composition (31 elements or variables), I can get >> reasonable separation of my 6 sources. Then when I go onto do LDA with >> the >> 6 groups, I get excellent separation. >> >> I feel like I should be reducing the variables to thos that are providing >> the most discrimination between the groups as this is important >> information >> for me. I struggle to interpret the PCA plot in a way that helps me (due >> to >> the large number of elements). So I'm trying to do some sort of >> step-wise >> variable selection. >> >> I would love to hear from someone (possibly a geochemist or similar) who >> does this regularly to determine the best course of action in R to do >> this. >> >> >> Thanks very much >> >> >> -- >> Gareth Campbell >> PhD Candidate >> The University of Auckland >> >> P +649 815 3670 >> M +6421 256 3511 >> E [EMAIL PROTECTED] >> [EMAIL PROTECTED] >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help@r-project.org 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. >> >> > > -- View this message in context: http://www.nabble.com/Variable-Selection-for-data-reduction-and-discriminant-anlaysis-tp19591270p19602702.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org 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.
