Thanks Bill,
Yes in my data too I get some different posteriors also.  I guess what I was
getting at was that when I'm dealing with multi-elemental data with 16
variables a probability of 1 is a bit more "certain" an assignment than I
was expecting.  I'm a student, so perhaps I don't have quite the handle on
the theory that I should.  I'm attempting to explain some of the
classifications in my write-up so was just looking for some discussion
really.

Thanks for your help.


Gareth Campbell
PhD Candidate
The University of Auckland

P    +649 815 3670
M    +6421 256 3511
E   gareth.campbell@esr.cri.nz
    gcam032@gmail.com


2009/4/12 <Bill.Venables@csiro.au>

> If you try
>
> library(MASS)
> example(lda)
> predict(z)$posterior
>
> You will see there are very many posterior probabilities there, too, which
> differe from 1 by round-off error at most.  So your data are not unique.
>
> I don't get why you don't get it.
>
>
> Bill Venables
> http://www.cmis.csiro.au/bill.venables/
>
>
> -----Original Message-----
> From: r-help-bounces@r-project.org [mailto:r-help-bounces@r-project.org]
> On Behalf Of gcam032
> Sent: Sunday, 12 April 2009 11:16 AM
> To: r-help@r-project.org
> Subject: [R] predict.lda posteriors equal to 1
>
>
> Hi all,
>
> I was wondering if anyone was able to explain to me why when I run
> predict.lda() on some new data I get lots of posterior probabilities = 1.
> This seems markedly unrealistic.  What do the posteriors mean in the
> context
> of the predict.lda() function?
>
> Thanks
>
> Gareth
> --
> View this message in context:
> http://www.nabble.com/predict.lda-posteriors-equal-to-1-tp23007165p23007165.html
> Sent from the R help mailing list archive at Nabble.com.
>
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