NaNs produced in the process of maximum likelihood
lgj200306 <lgj200306 at ...> writes:
Thaks David very much, but how can I improve my model? Should I change my likelihood function or do some thing else? Best wishes for all list members!
Your choices are: (1) if the final result looks sensible, and none of the final predicted values lead to NA/NaN results, you can *probably* ignore these warnings (2) depending on your model, you may be able to bound some of the parameters (e.g. using method="L-BFGS-B" and specifying lower/upper values) *or* fit them on a different scale (e.g. the log scale) to prevent zero/negative predicted values of lambda (predicted values of zero will be OK as long as they always go with observed values of zero -- otherwise you'll get infinite (negative) log-likelihood values. There are some simple examples of bounded optimization in the ?mle2 examples ...
At 2011-10-21 04:05:10,"David Valentim Dias" <dvdscripter <at> gmail.com> wrote: Should be a bad parameter like you get from dpois(1, -1) 2011/10/20 lgj200306<lgj200306 <at> 163.com> Hi, all I have a problem about the log maximum likelihood. I want to estimate several parameters using log maximum likelihood method (mle2() in package "bbmle" ), and the likelihood fucton was based on poisson distribution. When finished, there were some warnings said that:
>In dpois(x, lambda, log) : NaNs produced
Will this situation influence my result of parameters estimating or not? If my parameters estimated have been influenced, how can I improve my R code or data to achieve a exact estimating? Thanks for you attention and best wishes for all of you! Guojun Lin South China Botanical Garden, Chinese Academy of Science, China Department of Renewable Resources, University of Alberta, Canada