Hi, I am using the glm.nb" model type to fit some count data with TWO offset variables.
I have successfully used this approach to build scores of models for several datasets but I am having problems with two in particular. Depending on the model I try to fit for a given dataset, I obtain the following errors/warnings: Error: NA/NaN/Inf in foreign function call (arg 1) or glm.fit: algorithm did not converge or alternation limit reached When I remove one of those offset variables (the one I am less confident in) no error/warning seems to occur. I have checked for zero values, 1 values (as log is used), nas but everything looks fine. My gut feeling is that one of the offset variables makes the model unstable. However, the same approach was succesfully used to fit some other models with different datasets. The question is perhaps, can observational data that appears to be reasonable (at least in comparison to other datasets that i previously used) make the use of an offset variable so unstable? Any thoughts or advice on ANY aspect are much appreciated. Many thanks -- View this message in context: http://r.789695.n4.nabble.com/offset-glm-nb-issues-why-so-unstable-tp4631730.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.
