compositional data: percent values sum up to 1

Eh?  The original message says it's the design matrix that is perfectly
collinear after the transformation, not the response.

I don't know much about this type of data, but seems like you could just fit
the model w/o intercept to eliminate the collinearity, no?  It's the
interpretation of the result that may be tricky, I think.

Andy

-----Original Message-----
From: Spencer Graves [mailto:spencer.graves at pdf.com]
Sent: Monday, June 02, 2003 9:33 AM
To: Christoph Lehmann
Cc: Spencer Graves; r-help at stat.math.ethz.ch
Subject: Re: [R] compositional data: percent values sum up to 1


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