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 > >> > >>______________________________________________ > >>[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 > -- Brian D. Ripley, [EMAIL PROTECTED] 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) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
