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