Quasi Poisson for glmm
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 at 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 at gmail.com
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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
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