Message-ID: <DBBP189MB1340AF00DE87BA59C8D41DAEFE979@DBBP189MB1340.EURP189.PROD.OUTLOOK.COM>
Date: 2021-03-04T15:26:15Z
From: Kamal Atmeh
Subject: MCMCglmm interaction and posterior mode
In-Reply-To: <CAJtCY7V92MjygOPt6L1Et8i=jYOkor3dmhWcc1H+K5qEgq-=Vw@mail.gmail.com>
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 ? 23:17, Walid Crampton-Mawass a ?crit?:
> 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 Qu?bec at Trois-Rivi?res
> 3351, boul. des Forges, C.P. 500
> Trois-Rivi?res (Qu?bec) G9A 5H7
> Telephone: 819-376-5011 poste 3384
>
>
> On Wed, Mar 3, 2021 at 4:19 PM Kamal Atmeh <kamal.atmeh at hotmail.com
> <mailto:kamal.atmeh at 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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