[R] Variable Selection for data reduction and discriminant anlaysis

gcam032 gcam032 at gmail.com
Mon Sep 22 01:00:47 CEST 2008

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.



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 gareth.campbell at esr.cri.nz
> gcam032 at gmail.com
> 	[[alternative HTML version deleted]]
> ______________________________________________
> R-help at 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-tp19591270p19599461.html
Sent from the R help mailing list archive at Nabble.com.

More information about the R-help mailing list