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: http://www.nabble.com/Likelihood-Function-for-Multinomial-Logistic-Regression-and-its-partial-derivatives-tp24772731p24772731.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.
