Meta-analysis for heritability using MCMCglmm?

Hi Jackie,

1/ For the residuals you can do:

my_data$y-predict(my_model)

In the code above the predictions are marginal with respect to the  
random effects. If you want the predictions to include the random  
effects then use:

my_data$y-predict(my_model, marginal=NULL)

2/ I don't have a good solution for the weighted analysis. In fact we  
didn't do a weighted analysis in the end because only about 50% of  
studies provided standard errors. We figured that we lost more power  
by discarding the 50% than we would gain by incorporating information  
about the relative precision of different estimates. REML standard  
errors are based on the sampling distribution of estimates tending to  
the normal as n goes to infinity. In reality the sampling distribution  
will be skewed (particularly when h2 and/or sample size is low) like  
the posterior distribution. Its far from perfect but you could use the  
standard error given by:

0.5*((u95-pm)/qnorm(0.975)+(l95-pm)/qnorm(0.025))

where l95 and u95 are the lower and upper 95% credible intervals and  
pm is the posterior mean/mode.  The motivation behind this is *if* the  
posterior was normal and the asymmetry in the 95% credible intervals  
was just due to Monte Carlo error then this would be the best estimate  
of the posterior standard deviation (sort of like a standard error)  
given the information at hand. Not great justification!

3/ DIC (as focussed in MCMCglmm) is not a reliable model selection  
criterion for scientific inference. For Gaussian resopnses you could  
refocus DIC at the correct level post-analysis and then it would be  
more reliable. I should probably write a function to do this ....

Cheers,

Jarrod

Quoting Jackie Wood <jackiewood7 at gmail.com> on Sun, 1 Feb 2015 12:10:51 -0500:

Meta-analysis for heritability using MCMCglmm?

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