[R-sig-ME] Quasi Poisson for glmm

[Ben Bolker]

    I agree with Thierry that the binomial is a good start.

    If you do find that there is overdispersion in your binomial model, 
there are (at least) three possible approaches (see 
http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#overdispersion ):

   * beta-binomial model
   * observation-level random effects in a binomial model
   * quasi-binomial

   The last one is not available in glmmTMB, but the GLMM FAQ 
http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html shows you how to 
get quasi-likelihood results if you want.

   cheers
     Ben Bolker


On 12/3/20 10:55 AM, Thierry Onkelinx via R-sig-mixed-models wrote:
> Dear Faith,
> 
> I'd recommend starting with a full model with binomial distribution. What
> you perceive as overdispersion in the response is often modelled by the
> covariates.
> 
> 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 16:17 schreef Ebhodaghe Faith <ebhodaghefaith using gmail.com
>> :
> 
>> 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
>>>>>>
>>>>>
> 
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