[R-sig-ME] model building

[Thierry Onkelinx]

Dear Davide,

Your model formulation is OK.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2015-10-29 20:25 GMT+01:00 Davide Guido via R-sig-mixed-models <
r-sig-mixed-models op r-project.org>:

> Hello Everyone!
>
> I have a dataset with 150.000 statistical units (subjects) and 5 variables:
>
> - Binary outcome (0/1) (y)
> - municipality (string) (25 small areas)
> - gender
> - age
> - copper concentration (in ppm) (25 level, one by municipality)
>
> The last one, i.e. copper concentration, has been revealed per municipality
> (25 levels) and it is defined as municipalty mean of the different
> municipal
> sampling sites. I'm interested to the (conditional) copper effect on
> outcome
> and I have tried to specify this GLMM:
>
> gLMM <- glmer (y ~ gender + age + copper + (1 | municipality), family="
> binomial", data=datiSM)
>
>
> Is it correct fit a model containing both disaggregated and aggregated
> variables?
>
> Unfortunately, I cannot measure the copper at disaggregated level (by
> subject).
>
> Thanks in advance
>
> Davide
>
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