[R-sig-ME] nlme and NONMEM
r.j.forsyth at newcastle.ac.uk
Thu Nov 1 13:42:44 CET 2007
I'd appreciate hearing from anyone (off list if you think it more
appropriate) who can share their comparative experiences of non-
linear mixed effects modelling with both nlme and NONMEM. The latter
appears the traditional tool of choice particularly in pharmacology.
Having built up some familiarity with nlme I am now collaborating (on
a non-pharmacological project) with someone strongly encouraging me
to move to NONMEM, although that clearly represents another
considerable learning curve. The main argument in favour is the
relative difficulty I have had in getting convergence with nlme
models in my relatively sparse datasets particularly when (as in my
case) I am interested in the random effects covariance matrix and
wish to avoid having to coerce it using pdDiag().
I note the following comment from Douglas Bates on the R-help archive
> The nonlinear optimization codes used by S-PLUS and R are different.
> There are advantages to the code used in R relative to the code used
> in S-PLUS but there are also disadvantages. One of the disadvantages
> is that the code in R will try very large steps during its initial
> exploration phase then it gets trapped in remote regions of the
> parameter space. For nlme this means that the estimate of the
> variance-covariance matrix of the random effects becomes singular.
> Recent versions of the nlme library for R have a subdirectory called
> scripts that contains R scripts for the examples from each of the
> chapters in our book. If you check them you will see that not all of
> the nonlinear examples work in the R version of nlme. We plan to
> modify the choice of starting estimates and the internal algorithms to
> improve this but it is a long and laborious process. I ask for your
Can Doug or anyone comment on whether the development work on
lme4:::nlmer has included any steps in this direction or not?
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