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
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