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

[Walid Crampton-Mawass]

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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