jakub kreisinger <jakubkreisinger <at> seznam.cz> writes: > I am trying to analyze growth data on mice. To do this I attempted to fit logistic curve using nlme package. > However, the dataset I use is large (in total ca 20 000 measures on ca 3 000 individuals) with relatively > complicated structure (several explanatory variables with interactions + random effect where > individual offspring are nested within particular litters are nested within particular parental > pairs). > Although I had no problems to fit models, where the complexity of random effect was reduce (which is > conceptually incorrect), the fitting procedure of the full model did not reach the 1 iteration after > several hours. Do you have any idea how to solve this problem?
1) Get better starting values by using nlsList 2) Get still better starting values by fitting a simpler model first with nlme. This is VERY successful for me, it made many problems feasibly that blew up otherwise. Good luck in finding the right syntax for complex start values, this can be a huge challenge. 3) Use lmer in lme4. Your mileage may vary, I could not find a speedup for my problems, but larger problem might give one. 4) Use C for the core function. This is very effective, and there is at least on example coming with nlme (was it SSlogist?). Dieter ______________________________________________ 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.
