[R-sig-ME] MCMCglmm and multimembership analysis
ro@lyn@d@kin @ending from gm@il@com
Fri Oct 12 20:12:22 CEST 2018
I have two questions about MCMCglmm and multimembership models (aka
multiple membership models).
I am using these models to study social influence. In my scenario, the
focal individuals express a phenotype (the dependent variable) depending on
their social environment. The social environment is made up of other
individuals. For example:
MCMCglmm(phenotype ~ 1, random = ~focalID + mm(partnerID1 + partnerID2 +
...but the partners are also weighted (i.e., some interact more closely
with the focal than others).
Q1: Can MCMCglmm specify weights for the multimembership structure?
I have been able to do this in brms, but I haven’t seen an example for how
the weights could be done in MCMCglmm.
The second part of my question stems from the fact that a given individual
can act both as a focal and a partner, and I am specifically interested in
the covariance between these two sources of variation. (i.e., I would like
to be able to evaluate whether IDs that score highly on the phenotype also
elicit higher levels of the phenotype in others, using the same model.) In
a simpler, non-multimembership scenario, I would model this covariance as:
MCMCglmm(phenotype ~ 1, random = ~str(focalID + partnerID), rcov = ~units,
Q2: Can MCMCglmm accommodate the covariance between a single
multimembership structure and focalID, in the same model?
…e.g., something like this:
MCMCglmm(phenotype ~ 1, random = ~str(focalID + mm(partnerID1 + partnerID2
+ partnerID3)), rock = ~units, data=data)
I have verified that brms doesn't currently have the functionality to do
Any guidance is greatly appreciated.
Roslyn Dakin, PhD
Smithsonian Conservation Biology Institute
and the University of Ottawa
Website and blog: roslyndakin.com
Email: roslyn.dakin using gmail.com
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