Hi Sebastian,

You could try making "id" into a factor before running the model.  I've
noticed that when I have more than one random effect in a model, I have to
explicitly make each random covariate into a factor before running the model
otherwise I receive a message similar to yours.

Jesse



On 4/4/08, Douglas Bates <bates@stat.wisc.edu> wrote:
>
> On Thu, Apr 3, 2008 at 6:32 PM, Sebastian P. Luque <spluque@gmail.com>
> wrote:
> > On Thu, 3 Apr 2008 17:32:40 -0500,
> >  "Douglas Bates" <bates@stat.wisc.edu> wrote:
> >
> >  [...]
> >
> >
> >  > It seems that the observations are indexed by subject and treatment
> so
> >  > the number of levels in the factor treatment:id equals the number of
> >  > observations.  You can't estimate a variance for such a term and also
> >  > estimate a residual variance.
> >
> >  > I would start with
> >
> >  > n ~ treatment * community +(1|id)
> >
> >  Yes, the observations are indexed by subject and treatment in the sense
> >  that id levels are the same within treatments of the same community,
> but
> >  are different among communities.  This is a subset of the data:
> >
> >  ---<---------------cut here---------------start-------------->---
> >  id  community treatment     n
> >   1          A         1 13.93
> >   2          A         1 14.42
> >   3          A         1 13.56
> >   1          A         2 14.61
> >   2          A         2 14.74
> >   3          A         2 15.59
> >   1          A         3 13.95
> >   2          A         3 15.21
> >   3          A         3 14.51
> >   1          A         4 13.61
> >   2          A         4 14.99
> >   3          A         4 15.13
> >   4          B         1 14.79
> >   5          B         1 13.41
> >   6          B         1 14.71
> >   4          B         2 14.69
> >   5          B         2 13.46
> >   6          B         2 14.28
> >   4          B         3 14.30
> >   5          B         3 13.18
> >   6          B         3 13.58
> >   4          B         4 14.54
> >   5          B         4 13.25
> >   6          B         4 14.09
> >  ---<---------------cut here---------------end---------------->---
> >
> >  Of course, there are many more individuals, but the levels of id differ
> >  among communities, and are the same among treatments.  lmer did
> converge
> >  rapidly with your suggested formula though:
> >
> >  ---<---------------cut here---------------start-------------->---
> >  Linear mixed-effects model fit by REML
> >  Formula: n ~ treatment * community + (1 | id)
> >    Data: isotope.m.ph
> >   AIC BIC logLik MLdeviance REMLdeviance
> >   450 481   -216        410          432
> >  Random effects:
> >   Groups   Name        Variance Std.Dev.
> >   id       (Intercept) 0.193    0.439
> >   Residual             0.232    0.481
> >  number of obs: 240, groups: id, 61
> >
> >  Fixed effects:
> >                                Estimate Std. Error t value
> >  (Intercept)                     14.9748     0.1170   128.0
> >  treatment2                       0.0884     0.1222     0.7
> >  treatment3                      -0.2829     0.1222    -2.3
> >  treatment4                       0.3568     0.1222     2.9
> >  communitysanikiluaq             -0.5749     0.1678    -3.4
> >  treatment2:communitysanikiluaq  -0.2471     0.1763    -1.4
> >  treatment3:communitysanikiluaq  -0.7479     0.1763    -4.2
> >  treatment4:communitysanikiluaq  -0.6169     0.1763    -3.5
> >
> >  Correlation of Fixed Effects:
> >             (Intr) trtmn2 trtmn3 trtmn4 cmmnty trtm2: trtm3:
> >  treatment2  -0.522
> >  treatment3  -0.522  0.500
> >  treatment4  -0.522  0.500  0.500
> >  cmmntysnklq -0.697  0.364  0.364  0.364
> >  trtmnt2:cmm  0.362 -0.693 -0.347 -0.347 -0.524
> >  trtmnt3:cmm  0.362 -0.347 -0.693 -0.347 -0.524  0.503
> >  trtmnt4:cmm  0.362 -0.347 -0.347 -0.693 -0.524  0.503  0.503
> >  ---<---------------cut here---------------end---------------->---
> >
> >
> >  However, I don't understand how (1 | id) accounts for treatment being
> >  nested within community.  Maybe it's time for me to re-read some more
> >  examples from "Mixed-effects models in S and S-plus".  Thanks.
>
> I'm not sure that I understand what you mean by "treatment being
> nested within community".  Does this mean that there are really 8
> different treatments because treatment 1 in community A is different
> from treatment 1 in community B?  If so, then it would make sense to
> me to simply create a new factor that is the interaction of treatment
> and community.
>
> Perhaps I am approaching the community factor incorrectly.  In your
> data there are two communities so, even if it would be reasonable to
> model community effects as random effects, that would be difficult.
> With only two levels I think it is best modeled as a fixed effect,
> which would mean that questions about treatment and community are
> related to the fixed effects.
>
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