[R-sig-ME] Binomial model variance and repeatability estimates with MCMCglmm
j.hadfield at ed.ac.uk
Wed Oct 22 08:15:38 CEST 2014
The simulation only had one trial so it was equivalent to categorical.
If you up the number of trials then you can estimate the
You might try parameter expanded priors to remedy the last problem.
Quoting Ned Dochtermann <ned.dochtermann at gmail.com> on Tue, 21 Oct
2014 17:00:12 -0500:
> Thanks, I was aware of that for categorical and some of the other
> families but thought I could get away with it here and I wasn't
> quite sure otherwise how to calculate the relevant ratio (thanks for
> providing that too).
> With smaller sample sizes repeatability still seems to get
> misestimated and stuck close to zero even with long runs but running
> multiple chains seem to resolve that.
> Thanks again,
> On 10/21/2014 3:05 PM, Jarrod Hadfield wrote:
>> The residual variance of a binary response cannot be estimated, so use
>> prior1 = list(R = list(V = 1, fix=1),
>> G = list(G1 = list(V = 1, nu = 0.002)))
>> In this example it is more efficient to aggregate success/failures
>> of an individual into a multi-trial binomial response and use:
>> prior2 = list(R = list(V = 1, nu=0.002))
>> family="multinomial2", prior=prior2,
>> nitt = 260000, thin = 200, burnin = 60000,
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