I meant fitting not maximising, it is a nonlinear mixed effects model, with both fixed and random effects. My assumption is that for the function I am using the approximation approach used in nlme is not quite close enough, and nothing much that I can do, except for looking at starting values. I was hoping that someone would have other suggestions, so I will keep attempting to understand the control parameters. I can add an extra parameter to the model and obtain a worse fit.
Ken Dieter Menne writes: > >> >> I'm maximising a reasonably complex function using nlme (version >> 3.1-65, have also tried 3.1-66) and am having trouble with fixed >> parameter estimates slightly away from the maximum of the log >> likelihood. I have profiled the log likelihood and it is a parabola >> but with sum dips. Interestingly changing the parameterisation moves >> the dips around slightly. Unfortunately the PNLS step is finding a >> maximum at the dips rather than the mle. I have tried using starting >> values for the fixed parameters without change. Any ideas ? > > Ken, > > you should not use nlme for "maximising a complex function", > because it's a > rather specialized tool for mixed-model statistical analysis. Try > to use optim > directly, which has quite a few methods to choose from, and one of > them might > work for your problem. > > 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
