Dear list members,



I attempting to investigate the effects of food availability in a given year
on individual survival using the MCMCglmm package, and I'm running into
problems with model mixing.


Food data is available for a subset of the overall study period, and so some
individuals are born before the food data begins, and some do not die until
after the food data ends- therefore, not every individual is sampled across
their entire lifespan, and not every individual dies. I am using family =
categorical, and my response variable is binomial (survived or died), with
every individual having a record for every year of life that falls within
the period of food data. Therefore, the vast majority of responses are “1”
(28,636 out of 29,577). I am using random effects of individual identity,
maternal identity, and the year of sampling, all of which are substantial
(posterior distribution >> 0). I imagine that a sensible prior would be to
fix the residual variance at 1, and to apply an inverse-gamma prior to the
other random effects. However, this, and a number of other priors that I’ve
tried have produced high autocorrelation (in the order of >0.7) for the
variance components and low effective sample size (albeit using only 13,000
iterations). I wonder whether the problem is the sheer number of 1’s in the
dataframe, and whether the solution is in specifying a better prior, or
simply in running the analysis for much longer. Any advice from anyone with
experience of analysis of this type of data or these types of problems would
be gratefully received.



Best regards,

Adam

-- 
Adam Hayward
Post-Doctoral Research Associate
Department of Animal and Plant Sciences
Alfred Denny Building
University of Sheffield
Western Bank
Sheffield S10 2TN
UK

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