Dear all,
I am working on a data set in which I have sequentially measured egg
temperatures ("eggtemp") in birds incubating in different ambient
temperatures ("treat", sample data set below), "id" is not replicated within
treatment.
id treat eggtemp
1 79 3 30.90166
2 42 3 34.94044
3 10 3 32.69945
4 206 3 36.64127
5 23 3 31.80055
6 5 3 29.98338
7 24 3 35.72992
8 45 3 30.49584
9 29 3 33.64958
10 78 3 31.37673
11 44 3 32.85873
12 368 3 34.44875
13 79 4 31.24100
14 42 4 34.11634
15 10 4 34.73407
16 206 4 36.20914
17 23 4 34.98061
18 5 4 34.17590
19 24 4 37.71468
20 45 4 35.34765
21 29 4 35.48892
22 78 4 33.26593
23 44 4 34.86981
24 368 4 34.44875
25 79 2 27.33241
26 42 2 30.73269
27 10 2 29.54986
28 206 2 31.78947
29 23 2 29.69114
30 24 2 36.48199
31 45 2 29.76454
32 29 2 30.56510
33 78 2 27.71468
For this data, I want to construct a model with a random intercept and slope
to compare with a model containing only the random intercept to check
whether or not different individuals respond differently to treatment.
> temp2.lme<-lme(eggtemp~treat,random=~treat|id,data=temp3.df)
However, I get the below error, which I suspect might be because individuals
are not replicated within treatments.
> nlminb problem, convergence error code = 1
message = iteration limit reached without convergence (9)
If I specify treatment as a factor (which it really is not, just different
ambient temperatures), the model seems to converge. Similarly, the model
converges if I exclude one of the treatment levels. However, this does not
feel very satisfactorily.
So, how do I move on from here? Any tips and hints are much appreciated. I
have tried to include the random slope only, which also works, but the
inference for such a model would be quite different and not really what I am
after.
> temp3.lme<-lme(eggtemp~treat,random=~treat-1|id,data=temp3.df)
Kind regards,
Andreas Nord
Lund University
Sweden
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