René, Yes, to fit a re-parameterized logistic model I think you'd have to code the whole enchilada yourself, not relying on glm (but not nls() as nls() deals with least squares minimization whereas here we want to minimize a minus log binomial likelihood).
I did that and have the re-parameterized logistic model in a package I wrote for a colleague (this package has the logistic fit fully functional and documented). My code though only considers one continuous predictor. If you want I may email you this package and you figure out how to deal with the categorical predictor. One thing I believe at this point is that you'd have to do the inference on the continuous predictor _conditional_ on certain level(s) of the categorical predictor. Rubén -----Mensaje original----- De: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] En nombre de René Mayer Enviado el: jueves, 01 de diciembre de 2011 20:34 Para: David Winsemius CC: r-help Help Asunto: Re: [R] how to get inflection point in binomial glm Thanks David and Rubén! @David: indeed 15 betas I forgot the interaction terms, thanks for correcting! @Rubén: the re-parameterize would be done within nls()? how to do this practically with including the factor predictor? and yes, we can solve within each group for Y=0 getting 0=b0+b1*X |-b0 -b0=b1*X |/b1 -b0/b1=X but I was hoping there might a more general solution for the case of multiple logistic regression. HTH René Zitat von "David Winsemius" <dwinsem...@comcast.net>: > > On Dec 1, 2011, at 8:24 AM, René Mayer wrote: > >> Dear All, >> >> I have a binomial response with one continuous predictor (d) and one >> factor (g) (8 levels dummy-coded). >> >> glm(resp~d*g, data, family=binomial) >> >> Y=b0+b1*X1+b2*X2 ... b7*X7 > > Dear Dr Mayer; > > I think it might be a bit more complex than that. I think you should > get 15 betas rather than 8. Have you done it? > >> >> how can I get the inflection point per group, e.g., P(d)=.5 > > Wouldn't that just be at d=1/beta in each group? (Thinking, perhaps > naively, in the case of X=X1 that > > (Pr[y==1])/(1-Pr[y==1])) = 1 = exp( beta *d*(X==X1) ) # all other > terms = 0 > > And taking the log of both sides, and then use "middle school" math to solve. > > Oh, wait. Muffed my first try on that for sure. Need to add back both > the constant intercept and the baseline "d" coefficient for the > non-b0 levels. > > (Pr[y==1])/(1-Pr[y==1])) = 1 = exp( beta_0 + beta_d_0*d + > beta_n + beta_d_n *d*(X==Xn) ) > > And just > > (Pr[y==1])/(1-Pr[y==1])) = 1 = exp( beta_0 + beta_d_0*d ) # for the > reference level. > > This felt like an exam question in my categorical analysis course 25 > years ago. (Might have gotten partial credit for my first stab, > depending on how forgiving the TA was that night.) > >> >> I would be grateful for any help. >> >> Thanks in advance, >> René >> > -- > > David Winsemius, MD > West Hartford, CT > > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.