[R-sig-ME] Quasi Poisson for glmm
Thierry Onkelinx
th|erry@onke||nx @end|ng |rom |nbo@be
Thu Dec 3 11:32:36 CET 2020
Dear Faith,
You can use a negative binomial distribution.
Best regards,
ir. Thierry Onkelinx
Statisticus / Statistician
Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be
///////////////////////////////////////////////////////////////////////////////////////////
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
///////////////////////////////////////////////////////////////////////////////////////////
<https://www.inbo.be>
Op do 3 dec. 2020 om 11:24 schreef Ebhodaghe Faith <ebhodaghefaith using gmail.com
>:
> Hi All.
>
> I have a dataset for wish I intend to model an over-dispersed proportion
> response variable with hierarchical structure. I tried using the Quasi
> Poisson family, but available packages including glmmTMB do not allow this.
> What do you advice?
>
> Thanks in advance for your kind response.
>
> Faith Ebhodaghe
> Nairobi, Kenya
>
> [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-mixed-models using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
[[alternative HTML version deleted]]
More information about the R-sig-mixed-models
mailing list