1. Post on R-sig-mixed-models instead. Much more expertise and relevance there.
2. I would forget about mixed effects and treat the locations as
fixed. With only 5, you don't have enough information to estimate the
variance component with any precision anyway.
3. Feel free to ignore (2) and defer to the experts at (1).
Cheers,
Bert
On Tue, Sep 25, 2012 at 8:52 AM, Christof Klu? <ckluss at
email.uni-kiel.de> wrote:> Hi,
>
> I want to fit nonlinear dose-response curves, as "fun(X,a,b,c)",
for
> each of our 5 trail locations. Our data basis is something like
>
> location plot year dose response
>
> For each location there are 4 plots as repetitions (over 3 years). So
> the interactions "location*year" and "location*plot"
should be random
> effects.
>
> There are some examples in "Mixed-Effects Models in S and S-PLUS"
> (Pinheiro and Bates), but I do not see how they can help me for my
> model. Of course I can start with something like
>
> mod <- nlme(response ~ fun(dose,a,b,c)
> , fixed = list(a ~ 1, b ~ 1, c ~ 1)
> , random = list(a ~ 1, b ~ 1, c ~ 1)
> , groups = ~location
> , data=dat
> , start= ... )
>
> But that is not what I want. How do you describe that you want one fit
> for each of the five locations and that "location*year" and
> "location*plot" or something similar are random effects?
>
> Do you have some other examples that fit better to this problem setting?
> I welcome any tips.
>
> thx
> Christof
>
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--
Bert Gunter
Genentech Nonclinical Biostatistics
Internal Contact Info:
Phone: 467-7374
Website:
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