[R-sig-ME] nlme and NONMEM

[Rob Forsyth]

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
> patience.
>

Can Doug or anyone comment on whether the development work on  
lme4:::nlmer has included any steps in this direction or not?

Thanks

Rob Forsyth