I am wanting to use a multimembership MCMCglmm with the following model
nsamp <- 2000
THIN <- 100
BURNIN <- 3000
NITT <- BURNIN + THIN*nsamp
mcModela <- MCMCglmm(cbind(count.tog,count.apart) ~ Sexpair +
Sexpair:(scale(TrioML.R)), random = ~ mm(ID1 + ID2),
nitt = NITT, thin = THIN, burnin = BURNIN,
family = "multinomial2", data = assort_b,
verbose = FALSE)
Sexpair has the following levels M_M, M_F and F_F. TrioML.R is a continuous covariate that I have mean centred. ID1 and ID2 indicate the individual identities for each dyadic observation. I haven't specified any priors so that all info comes from the data. This produces the following output
sols <- summary(mcModela)$solutions
sols
post.mean l-95% CI u-95% CI
(Intercept) -2.8694404 -4.660506 -0.8248128
SexpairF_F 0.9684242 -1.349167 3.2829986
SexpairM_M -32.5909713 -73.597824 -3.0423063
SexpairM_F:scale(TrioML.R) -1.1555533 -2.659659 0.1135262
SexpairF_F:scale(TrioML.R) 0.4839637 -1.309041 2.9091455
SexpairM_M:scale(TrioML.R) -72.5303991 -167.074322 1.0027751
eff.samp pMCMC
(Intercept) 1786.489094 0.0140
SexpairF_F 2121.444933 0.3650
SexpairM_M 9.047826 0.0005
SexpairM_F:scale(TrioML.R) 1553.295932 0.0450
SexpairF_F:scale(TrioML.R) 861.841779 0.7020
SexpairM_M:scale(TrioML.R) 9.691509 0.0050
I am aware that an effective sample of 100-1000 is ideal. As you can see for some covariates the effective sample size is much larger or close to 1000, while for other covariates it is quite low. I have tried adjusting the NITT, THIN, and BURNIN but the results are very similar (some covariates have a high eff. samp while others have a very low eff. samp).
For 'SexpairM_M' and 'SexpairM_M:scale(TrioML.R)' there's very poor mixing of the chain:
However, for all other covariates this seems to be ok. I think this might be because there is a lot of zeros for one of the response variables ('count.tog') for SexpairM_M - could this cause poor mixing of the chains?
and I get the following for autocorrelation of the chains
ID1+ID2. units
Lag 0 1.0000000 1.0000000
Lag 100 0.22808372 0.4594582
Lag 500 0.14334204 0.2974043
Lag 1000 0.09248625 0.2163000
Lag 5000 -0.01672504 0.0178789
My questions are:
(1) Is it essential to specify priors for this type of model, and, if so, how would I go about specifying priors for this type of model?
(2) Will refitting the model with priors specified increase the effective sample size for the covariates with a small effective sample?
(3) Should I be concerned about the interpretation of this model with some covariates with a low effective sample?
If I fit the model with just the interaction term and a second model without the interaction term I seem to get a better result with the 'SexpairM_M' and 'SexpairM_M:scale(TrioML.R)' having a higher effective sample and better mixing of the chain. Therefore, my last question is
(4) Would it be better to fit multiple models to increase the effective sample/mixing of the chain?
I haven't been able to find much help online with the multimembership type MCMCglmm and I am new to Bayesian analysis so any help would be greatly appreciated!