1) What version of lmer are you running? The new version post 23/6/08 copes
with difficult likelihoods better.
2) Have you changed anything in the elements of the control parameter. The
pre 23/6 verion had several parameters and the later one fewer. In
particular you can increase the iterations
3) Have you centred your x variables so they have means around zero. If not
you should always do this as it will make the fitting easier especially with
quadratic terms. This ought to have been my first suggestion.
4) Having another look at your model you say it is non-linear, but it looks
linear to me if you set the squared terms as covariates too.

Good luck

Gillian Raab
Edinburgh

On 03/07/2008, yufeng@nsm.umass.edu <yufeng@nsm.umass.edu> wrote:
>
> Dear R-user,
>
> I am trying to use the R "lmer" function in lme4 package to fit a non
> linear
> mixed effects model. The model I wand to fit is at an individual level with
> 4
> parameters. For all parameters both fixed and random effects have to be
> estimated, as well as their covariance matrix (see the formula bellow).
> y~x1+x1^2+x2+x2^2.
>
>
> I tried to fit the model with my data sets, but most of the time, R returns
> an
> error message.
> there are three main types of errors :
>
> - In mer_finalize(ans, verbose) :
>   function evaluation limit reached without convergence (9)
>
> -  there are false convergence (8)
>
> -there are singular convergence (7)
>
> Do you know how to resolve these problems. Is there a way to modify the
> parameters of the maximization algorithm to avoid these error messages?
>
> Thank you for your help and answers.
>
> Regards,
>
> Yufeng Zhang
>
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>



-- 
Gillian M Raab
10 Ainslie Place EH3 6AS
tel 0131 226 6234
mobile 07748 678 551

	[[alternative HTML version deleted]]


