Benedikt Gehr <benedikt.gehr <at> ieu.uzh.ch> writes: > > Hi there > > I am using mle2 for a multinomial likelihood optimization problem. My > function works fine when I'm using simulated data, however my cell > probabilities of the true data for the multinomial likelihood are > sometimes very small (in some cases <0.00...) and the estimated point > estimates fit the true vlaues quite poorly. Is there a way how to handle > near zero probabilities in maximum likelihood optimization? >
Hard to say without more detail. Can you send a reproducible example (your data, or a small subset of your data, or some way of simulating the data that *does* create the problem)? Since you're using log-likelihoods already (within mle2) the problem is unlikely (?) to be numerical -- R doesn't have any problem with very large negative log-likelihoods. Do your likelihood profiles look reasonable? If so then the problem is more likely that your model doesn't fit the data well than that you are having convergence problems. Have you considered the possibility of overdispersion (non-homogeneity/non-independence), e.g. via a Dirichlet-multinomial model? ______________________________________________ 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.
