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