[R-sig-ME] persistant autocorrelation in binomial MCMCglmm

Aiello, Christina caiello at usgs.gov
Thu May 4 02:24:02 CEST 2017

Dear list,

I'm very new to MCMCglmm but have done my best to read-up on Jarrod
Hadfield's package documents, tutorials and various examples posted online
and discussed on this forum. I'm having trouble fitting what I thought was
a fairly simple binomial mixed effects model using MCMCglmm. I'll start by
describing the data, then the model, then my problem and questions:

My dataset is comprised of unique dyads - pairs of animals located at one
of four sites (C1, C2, R1, R2). The response variable, 'contact' indicates
that the dyad did (1) or did not (0) interact over the course of the study.
The unique id of the members of the dyad are 'tort1' and 'tort2'. Because
individuals appear in multiple dyads, I've included a random effect for
tortID using the multiple membership function available in the package to
account for the non-independence of observations and the fact that some
individuals may have a tendency to contact more than others. For fixed
effects, in this simplified model I only have one categorical variable,
'site' (which I would have entered as a random effect but I only have 4
levels) and one continuous variable, 'overlap' - which is an estimate of
space-use similarity for each dyad. I centered and scaled this variable by
the non-zero mean value and standard deviation (though I've also tried the
model without centering). This may be relevant to my problem: 'overlap's
distribution is highly skewed and mostly zero values - similarly, the
response variable 'contact' is rare and characterized by mostly zeros.

> table(datafi$contact, datafi$site)
     C1  C2  R1  R2
  0 241 229 176 181
  1  35  24  14   9

The model:

pr<-list( R= list(V=1,  n=0, fix=1), G= list(G1=list(V=1, n=0.002)) )

m1 <- MCMCglmm(

fixed = contact ~ (1 + site + overlap ) ,

random = ~mm(tort1 +tort2),

data = datafi,

family = "categorical", verbose = FALSE,

pr=TRUE, pl=TRUE, prior=pr,

nitt=4100000 , thin=2000 , burnin= 100000

> summary(m1)

 Iterations = 100001:4098001
 Thinning interval  = 2000
 Sample size  = 2000

 DIC: 207.4525

 G-structure:  ~mm(tort1 + tort2)

            post.mean  l-95% CI u-95% CI eff.samp
tort1+tort2     2.128 0.0002693    5.488    414.6

 R-structure:  ~units

      post.mean l-95% CI u-95% CI eff.samp
units         1        1        1        0

 Location effects: contact ~ (1 + site + overlap)

            post.mean l-95% CI u-95% CI eff.samp  pMCMC
(Intercept)   -2.2102  -3.6055  -0.7437   1505.6  0.004 **
siteC2        -0.4143  -2.7572   1.4982   1808.4  0.708
siteR1        -1.2543  -4.0794   0.8424   1069.0  0.268
siteR2        -1.4753  -3.9300   0.9782   1348.9  0.205
overlap        3.9025   2.8273   5.1260    488.5 <5e-04 ***

As far as I can tell, the chains themselves look good and if I run multiple
chains and run the Gelman-Rubin diagnostic, the PSRF values are all 1 or
1.01. The parameter estimates are consistent and make sense. The problem
lies in the autocorrelation - large amounts in the 'overlap' variable and
many of the random intercepts. Here's a sample of the autocorr results:

> sort(diag(autocorr(m1$Sol)[2,,]))
##these are the worst offenders
  (Intercept)    tort1.4534    tort1.3719      tort1.33    tort1.3620
 tort1.30    tort1.3045
 0.0926484964  0.0938622549  0.1009204749  0.1049459123  0.1065261665
 0.1090179501  0.1237642453
       siteR2    tort1.3150    tort1.5579        siteR1    tort1.5473
 tort1.2051    tort1.3092
 0.1339370132  0.1359132027  0.1383816060  0.1506535457  0.1639062068
 0.1682852625  0.1683907054
   tort1.5044     tort1.804    tort1.5141    tort1.5103    tort1.4148
 tort1.4678    tort1.4428
 0.1752670493  0.1767909176  0.1865412328  0.1919929722  0.2257633018
 0.2318115800  0.2521806794
   tort1.3633    tort1.3335    tort1.5101    tort1.3043      tort1.26
 tort1.2014       tort1.6
 0.2577034325  0.2593673083  0.2602145001  0.2717718040  0.3487288823
 0.3748047689  0.4478979043

> autocorr.diag(m1$VCV)
          tort1+tort2 units
Lag 0      1.00000000   NaN
Lag 2000   0.58292962   NaN
Lag 10000  0.12771910   NaN
Lag 20000  0.05262786   NaN
Lag 1e+05  0.01757316   NaN

I've attempted to fit the model with both uninformative (shown above) and
parameter expanded priors (
pr2<-list( R= list(V=1, n=0, fix=1), G=list(G1=list(V=1, nu=1, alpha.mu=0,
alpha.V=1000)) )), with parameter expanded priors performing slightly
worse. I've attempted incrementally larger iteration, thinning, and burn in
values, increasing the thinning to as high as 2000 with a large burn-in
(100000) in hopes of improving convergence and reducing autocorrelation.
I've tried slice sampling and saw little improvement. Nothing I tried while
retaining this model structure improved the acfs. I checked the latent
variable estimates and all were under 20, with mean of -5.

The only way I was able to reduce the autocorrelation was to fit a model
without the random effect, which isn't ideal as I'm ignoring repeated
measures of individuals among dyads. I've read on this forum that random
effects in binomial models are notoriously hard to estimate with this
package and I've also read that one should not just increase thinning to
deal with the problem (MEE 2012 Link & Eaton
Interestingly, I have count responses association with all interacting
dyads and I can fit zero truncated models to those responses just fine with
the same fixed and random effects.

My questions are then:
1) Do you think there is something inherently wrong with the data or just
problems with the mixing algorithms in the context of this data?
2) Are there any other changes to the MCMCglmm specification I might try to
improve mixing? Or any problems with my current specification?
3) Any suggestions on where to go from here?

I would greatly appreciate any insights and happy to provide further info
as needed!


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