[R-sig-ME] Quasi Poisson for glmm

[Thierry Onkelinx]

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]]