Thanks a lot. The info about computing the gradient will be helpful. I admit that I am somewhat confused about the likelihood function itself. It is often said that you need to set a reference category. However, I found two different implementations in Matlab for which setting the reference category is not really obvious. Thus I wanted to find a single, indisputable implementation of the likelihood function for multinomous logistic regression in R. Can't believe there is no such available.
Nick Ravi Varadhan wrote: > > Hi, > > Providing the gradient function is generally a good idea in optimization; > however, it is not necessary. Almost all optimization routines will > compute this using a simple finite-difference approximation, if they are > not user-specified. If your function is very complicated, then you are > more likely to make a mistake in computing analytic gradient, although > many optimization routines also provide a check to see if the gradient is > correctly specified or not. But you can do this yourself using the `grad' > function in "numDeriv" package. > > Hope this is helpful, > Ravi. > > ____________________________________________________________________ > > Ravi Varadhan, Ph.D. > Assistant Professor, > Division of Geriatric Medicine and Gerontology > School of Medicine > Johns Hopkins University > > Ph. (410) 502-2619 > email: rvarad...@jhmi.edu > > > ----- Original Message ----- > From: nikolay12 <nikola...@gmail.com> > Date: Sunday, August 2, 2009 3:04 am > Subject: [R] Likelihood Function for Multinomial Logistic Regression and > its partial derivatives > To: r-help@r-project.org > > >> Hi, >> >> I would like to apply the L-BFGS optimization algorithm to compute >> the MLE >> of a multilevel multinomial Logistic Regression. >> >> The likelihood formula for this model has as one of the summands the >> formula >> for computing the likelihood of an ordinary (single-level) >> multinomial logit >> regression. So I would basically need the R implementation for this >> formula. >> The L-BFGS algorithm also requires computing the partial derivatives >> of that >> formula in respect to all parameters. I would appreciate if you can >> point me >> to existing implementations that can do the above. >> >> Nick >> >> PS. The long story for the above: >> >> My data is as follows: >> >> - a vector of observed values (lenght = D) of the dependent multinomial >> variable each element belonging to one of N levels of that variable >> >> - a matrix of corresponding observed values (O x P) of the independent >> variables (P in total, most of them are binary but also a few are >> integer-valued) >> >> - a vector of current estimates (or starting values) for the Beta >> coefficients of the independent variables (length = P). >> >> This data is available for 4 different pools. The partially-pooled model >> that I want to compute has as a likelihood function a sum of several >> elements, one being the classical likelihood function of a >> multinomial logit >> regression for each of the 4 pools. >> >> This is the same model as in Finkel and Manning "Hierarchical Bayesian >> Domain Adaptation" (2009). >> >> -- >> View this message in context: >> Sent from the R help mailing list archive at Nabble.com. >> >> ______________________________________________ >> R-help@r-project.org mailing list >> >> PLEASE do read the posting guide >> 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. > > -- View this message in context: http://www.nabble.com/Likelihood-Function-for-Multinomial-Logistic-Regression-and-its-partial-derivatives-tp24772731p24781298.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
