[R] compositional data: percent values sum up to 1

[Prof Brian Ripley]

On Mon, 2 Jun 2003, Spencer Graves wrote:

> "glm" will do multinomial logistic regression.  However, if J is large, 

Strictly, no, it will not as that is not a GLM.  glm() can only do it via 
surrogate Poisson models.  multinom in nnet(VR) will do multinomial 
logistic regression.

> I doubt if that will do what you want.  If it were my problem, I might 
> feel a need to read the code for "glm" and modify it to do what I want. 
>   Perhaps someone else can suggest something better.
> 
> hth.  spencer graves
> 
> Christoph Lehmann wrote:
> > I want to do a logistic regression analysis, and to compare with, a
> > discriminant analysis. The mentioned power maps are my exogenous data,
> > the dependent variable (not mentioned so far) is a diagnosis
> > (ill/healthy)
> > 
> > thanks for the interest and the help
> > 
> > Christoph
> > 
> > On Sun, 2003-06-01 at 21:01, Spencer Graves wrote:
> > 
> >>What are you trying to do?  What I would do with this depends on many 
> >>factors.
> >>
> >>spencer graves
> >>
> >>Christoph Lehmann wrote:
> >>
> >>>again, under another subject:
> >>>sorry, maybe an all too trivial question. But we have power data from J
> >>>frequency spectra and to have the same range for the data of all our
> >>>subjects, we just transformed them into % values, pseudo-code:
> >>>
> >>>power[i,j]=power[i,j]/sum(power[i,1:J])
> >>>
> >>>of course, now we have a perfect linear relationship in our x design-matrix,
> >>>since all power-values for each subject sum up to 1.
> >>>
> >>>How shall we solve this problem: just eliminate one column of x, or
> >>>introduce a restriction which says exactly that our power data sum up to
> >>>1 for each subject?
> >>>
> >>>Thanks a lot
> >>>
> >>>Christoph
> >>
> >>______________________________________________
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> >
> 
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-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
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