Thanks for the reply Ben. I have 16 subjects, however the number of observations per subject is not even, which is why I opted for nlmer rather than nlme. I presumed nlmer (as is the case for lmer) would be better at handling unbalanced designs.

I will check out the suggested books. Thanks.

Gabriel Yvon-Durocher 

> Gabriel Yvon-Durocher <g.yvon-durocher@...> writes:
> 
>> 
>> Dear all,
>> 
>> I am trying to fit a 3rd order polynomial to some rate and temperature
> measurements, but am running into
>> problems. I would like to treat the parameter a,b,c,d as random effects across
> subjects, and use the model
>> to estimate these parms for each experimental subject.
>> 
>> Here is my model:
>> 
>>    resp.fun<-function(Temp,a,b,c,d) -a*(Temp^3)+b*(Temp^2)-c*(Temp)+d
>> 
>> then I build a gradient function:
>> 
>>    gr.model<- deriv(body(resp.fun), namevec = c('a','b','c','d'),
> function.arg = resp.fun)
>> 
>> and then run the mixed effects model:
>> 
>>    model<-nlmer(log10.rate ~ gr.model(Temp,a,b,c,d) ~ 1+a+b+c+d|Subject,
>>    	start = c(a=4.897e-05, b=3.198e-03, c=3.569e-02, d=1),
>>    	data = lab.analysis,
>>    	na.action=na.omit)
>> 
>> unfortunately I get the error message 
>> 
>>    Warning message:
>>    In mer_finalize(ans) : false convergence (8)
>> 
>> Any ideas on what I may be doing wrong? 
>> I have manually fit this polynomial to the data for each subject and the
>> fits are good, but I want to run the mixed effects analysis to get the best 
>> possible parameter estimates
>> given the experimental design.
>> 
> 
>  How many subjects?
> 
>  Can you do it in nlme instead, which is more mature/robust?
>  If not, you may end up needing to do this in AD Model Builder, or BUGS,
> or rolling your own Laplace approximator (there is an example
> in Madsen and Thyregod's book) ...
> 
>  Ben Bolker
> 
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