[R-sig-ME] Ordinal categorical variable as a random effect in MCMCglmm

[Jarrod Hadfield]

Dear Manabu,


I suggest

prior<-list(R=list(V=1, nu=0.002), G=list(G1=list(V=1, nu=1, alpha.mu  
= 0, alpha.V = 25^2)))

m1<-MCMCglmm(counts~Quality, random=~Taxon, ginverse=list(Taxon=Ainv),  
family="poisson", prior=prior, ....)

as a first stab.  MCMCglmm can't handle ordered predictors, so you  
could try just fitting Quality as a standard factor, or fitting it as  
continuous?

Cheers,

Jarrod


Quoting Manabu Sakamoto <manabu.sakamoto at gmail.com> on Mon, 3 Nov 2014  
14:35:40 +0000:

> Dear Jarrod,
>
> Thanks for your response. Quality is a ranking in the
> completeness/missingness of the data; a higher quality data point is scored
> higher. This is an attempt to control for potential missing information
> from the response variable. The response is a count (hence the family being
> Poisson), and the idea is that for each taxon on a phylogeny, there is a
> count variable, but that count could potentially be under-estimated based
> on the Quality of the data associated with each taxon. But in reality, this
> potential under-estimation is unknown and unmeasurable so Quality is just
> one attempt to control for this somewhat-known uncertainty.
>
> many thanks,
> Manabu
>
> On 3 November 2014 14:22, Jarrod Hadfield <j.hadfield at ed.ac.uk> wrote:
>
>> Dear Manabu,
>>
>> Could you explain what Quality is? If it is ordered it is hard to see why
>> you would be fitting it as random effect? Is this really your response
>> variable?
>>
>> Cheers,
>>
>> Jarrod
>>
>>
>>
>>
>> Quoting Manabu Sakamoto <manabu.sakamoto at gmail.com> on Wed, 29 Oct 2014
>> 11:16:48 +0000:
>>
>>  Dear list,
>>>
>>> I'm using MCMCglmm with some random effects, including a phylogeny and a
>>> categorical variable, scored along an ordinal scale, e.g., 1 < 2 < 3 <...
>>>
>>> If my phylogenetic tip names are stored as a character string Taxon (and
>>> there is an associated inverse A object), and my quality codes are stored
>>> as an ordered factor variable Quality, then my questions are:
>>>
>>> 1) Can I specify the random effect formula simply as: random= ~ Taxon +
>>> Quality --- i.e., without functions like us() or idh() around Quality?
>>> 2) What sort of prior should I assign for Quality? --- For the moment I am
>>> using:
>>>
>>> list(V=1, nu=1, alpha.mu = 0, alpha.V = 25^2)
>>>
>>>
>>> I'd appreciate any advise.
>>>
>>> Kind regards.
>>> Manabu
>>> --
>>> Manabu Sakamoto, PhD
>>> manabu.sakamoto at gmail.com
>>>
>>>         [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-mixed-models at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>>
>>>
>>
>>
>> --
>> The University of Edinburgh is a charitable body, registered in
>> Scotland, with registration number SC005336.
>>
>>
>>
>
>
> --
> Manabu Sakamoto, PhD
> manabu.sakamoto at gmail.com
>


-- 
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.