[R-sig-ME] non-linear mixed effects: polynomial

[Gabriel Yvon-Durocher]

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.

Thanks in advance.