simulate.lme (nlme)
Your transcript looks peculiar. It shows require(lme) and you end up with the nlme package attached. Does that really work or is it a misprint? The reason for the small negative difference in the likelihood values is because of convergence on the boundary. It is possible for the difference in the negative log-likelihoods of the alternative and null models fit to the same data to be zero. This corresponds to the situation where the MLE or REML estimate of one of the variance components in the alternative model is zero. When using a numerical optimization technique that difference will not come out to be exactly zero, especially given the way that the optimization is done in nlme package, so you end up with a very small difference that can be negative or positive.
On 9/26/07, Andrzej Galecki <agalecki at umich.edu> wrote:
Dear All, simulate.lme generates an object (list) containing the MLs (and REMLs) for null and alternative models, fitted to simulated data. Since models are nested and are fitted to the same data we anticipate that elements of the vector difx containing ML differences , i.e. object$alt$ML - objectnull$ML, are positive. In the example below this difference is calculated for 30 simulations. I am wondering why some of the elements in difx vector are less than zero. Thank you Andrzej Galecki University of Michigan PS. Thank you Professor Bates for your response to my previous question.
> require(lme) > sessionInfo()
R version 2.5.0 (2007-04-23) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252 attached base packages: [1] "stats" "graphics" "grDevices" "utils" "datasets" "methods" "base" other attached packages: lattice nlme "0.15-4" "3.1-80"
> orthSim <-simulate.lme(list(fixed = distance ~ age, data = Orthodont,
+ random = ~ 1 | Subject), nsim=30, seed=12345, m2 = list(random = ~ age | Subject))
> difx <- orthSim$alt$ML - orthSim$null$ML > range(difx)
[1] -0.006097405 5.222863611
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