Douglas Bates <bates <at> stat.wisc.edu> writes: > > 3) Use lmer in lme4. Your mileage may vary, I could not find a speedup > > for my problems, but larger problem might give one. > > Did you mean nlmer in the lme4 package? If so, it may be worthwhile > trying the development branch but that is not something for the > faint-hearted.
Thanks, Doug, for you comments. To be fair, I wrote these unordered thoughts to get you out of the snowhole :-) > > 4) Use C for the core function. This is very effective, and there is at > > least > > on example coming with nlme (was it SSlogist?). > > Do you think that evaluation of the model function takes a substantial > portion of the computing time? I am asking for my interest, not > because I think I know the answer. So, for example, have you profiled > difficult nlme fits and found that the model function evaluation was > expensive? No, I did not profile that function, but 8 years ago tried it once because at that time I thought it would help. Nowadays, I am more inclined to think that failure of nlme with a more complex model is a failure of the model, not of nlme; I don't have speed problems with my data. However, I remember that in a similar case with ode/lsoda, using c function made a factor of 20++, so I would have a look into it again if speed was a concern for me. Dieter ______________________________________________ [email protected] 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.
