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:[EMAIL PROTECTED] > Sent: Monday, June 02, 2003 9:33 AM > To: Christoph Lehmann > Cc: Spencer Graves; [EMAIL PROTECTED] > 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 > >> > >>______________________________________________ > >>[EMAIL PROTECTED] mailing list > >>https://www.stat.math.ethz.ch/mailman/listinfo/r-help > > > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
