[R-sig-ME] MCMCglmm interaction and posterior mode

[Walid Crampton-Mawass]

Another option would be the QGglmm package by Pierre de Villemereuil
(2016), it has multiple functions to extract estimates but I wouldn't say
it is automatic as much as it is faster computationally to compute
estimates.

Good luck
-- 
Walid Crampton-Mawass
Ph.D. candidate in Evolutionary Biology
Population Genetics Laboratory
University of Québec at Trois-Rivières
3351, boul. des Forges, C.P. 500
Trois-Rivières (Québec) G9A 5H7
Telephone: 819-376-5011 poste 3384


On Thu, Mar 4, 2021 at 10:26 AM Kamal Atmeh <kamal.atmeh using hotmail.com> wrote:

> Hello Walid,
>
> Thank you for your prompt response!
>
> The at.level() function works perfectly and does exactly what I want. I
> have also modified the other 2-way interactions to only include the level
> of interest (H).
>
> As for the broom package, even broom. mixed, it seems that it mostly gives
> a tidy version of the model's output. I haven't found a function that does
> the automatic computation though, but I will look more thoroughly.
>
> Thanks again!
>
> Cheers,
>
> Kamal
> Le 03/03/2021 à 23:17, Walid Crampton-Mawass a écrit :
>
> Hello Kamal,
>
> One way to do this with MCMCglmm is to use the at.level() function. You
> can determine for which level of your categorical variable you want the
> interaction, ex. at.level(tactic,2):a.level(period,2):env.
>
> However, in doing so you are only estimating the coefficient for that
> specific interaction and ignoring the rest. That to me would seem a bit odd
> given that for the 2-way interactions you are still including the other
> levels, but of course it depends on what assumptions you are making for
> your model.
>
> and regarding automating the computation of the posterior mean, I think
> (not sure though) that the broom package offers some wrap functions for
> MCMCglmm and computing posterior estimates.
>
> Good luck
> --
> Walid Crampton-Mawass
> Ph.D. candidate in Evolutionary Biology
> Population Genetics Laboratory
> University of Québec at Trois-Rivières
> 3351, boul. des Forges, C.P. 500
> Trois-Rivières (Québec) G9A 5H7
> Telephone: 819-376-5011 poste 3384
>
>
> On Wed, Mar 3, 2021 at 4:19 PM Kamal Atmeh <kamal.atmeh using hotmail.com>
> wrote:
>
>> Dear all,
>>
>> I have some questions, which may sound trivial, pertaining to
>> interaction models with MCMCglmm.
>>
>> I am running the following model with a gaussian distribution and a
>> 3-way interaction between two categorical two-level variables (tactic:
>> F/H and period PB/B) and one continuous variable (env):
>>
>> model <- MCMCglmm(lD ~ tactic*period*env
>>                                       , random =
>> ~sp_phylo+species2+phylo_pop+phylo_popY+phylo_pop_id
>>                                       , family = "gaussian"
>>                                       , ginverse = list(sp_phylo =
>> inv.phylo$Ainv) # include a custom matrix for argument phylo
>>                                       , prior = prior1
>>                                       , data = Data
>>                                       , nitt = 22e+04
>>                                       , burnin = 20000
>>                                       , thin = 100
>>                                       , pr=TRUE)
>>
>> After looking at the results, I found that the 2-way interaction
>> tactic*env from the tactic*period*env interaction was not significant,
>> however the 3-way interaction itself was, with the following output in
>> the summary:
>>
>>  >>>   tacticH:periodB:env      0.17831  0.05360 0.30512     5000
>> 0.0052 ** (the intercept represents tactic F and period PB)
>>
>> I tried to run the model again in order to simplify it using ":" and
>> therefore remove the non-significant 2-way interaction:
>>
>> model2 <- MCMCglmm(lD ~ tactic*period + period*env + *tactic:period:env*
>>                                       , random =
>> ~sp_phylo+species2+phylo_pop+phylo_popY+phylo_pop_id
>>                                       , family = "gaussian"
>>                                       , ginverse = list(sp_phylo =
>> inv.phylo$Ainv) # include a custom matrix for argument phylo
>>                                       , prior = prior1
>>                                       , data = Data
>>                                       , nitt = 22e+04
>>                                       , burnin = 20000
>>                                       , thin = 100
>>                                       , pr=TRUE)
>>
>> When using ":", the output of my model returns the posterior mean for
>> each level of the categorical variables instead of one level as before:
>>
>> tacticF:periodPB:env -0.1668620 -0.3554264  0.0005143    195.0 0.0923 .
>> tacticF:periodB:env  -0.2018706 -0.3783204 -0.0174366    195.0 0.0410 *
>> tacticH:periodPB:env -0.1561097 -0.2066183 -0.1093840    118.2 <0.005 **
>>
>> How should I define the interaction in the model in order to obtain an
>> output similar to the one when the "*" interaction was used
>> (tacticH:periodB:env) while simplifying and removing the non-significant
>> interaction from the 3-way interaction?
>>
>> Finally, is there a way to automatically compute the posterior mean of
>> the continuous variable for each modality of the interaction?
>>
>> Thank you and stay safe!
>>
>> Kamal
>>
>>
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>>
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>>
>

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