[R-sig-ME] Quasi Poisson for glmm

[Ebhodaghe Faith]

Dear Thierry,
The proportions are on number of individuals infected by a parasite divided
by total number of individuals examined.

Thanks
Faith

On Thu, 3 Dec 2020, 4:48 p.m. Thierry Onkelinx, <thierry.onkelinx using inbo.be>
wrote:

> Dear Faith,
>
> I missed to see you have a proportion response. The negative binomial is a
> (better) alternative for the quasi Poisson. But they assume count data.
> What kind of proportions do you have? Is it based on a number of successes
> for a number of trials (binomial, beta binomial)? Or a continuous value
> between 0 and 1 (beta)?
>
> 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 13:14 schreef Ebhodaghe Faith <
> ebhodaghefaith using gmail.com>:
>
>> Thanks, Thierry.
>>
>> But could you please refer me to an article preferably in the biological
>> sciences where a negative binomial distribution was used to model an
>> over-dispersed multilevel proportion response variable?
>>
>> Thanks for your kind assistance.
>>
>> Regards
>> Faith
>>
>> On Thu, 3 Dec 2020, 1:32 p.m. Thierry Onkelinx, <thierry.onkelinx using inbo.be>
>> wrote:
>>
>>> 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]]