[R-sig-ME] nested random effect specification in MCMCglmm

[Sardell, Rebecca]

Hi,

I'm using MCMCglmm to run some binary and Poisson models and I'd just like to check whether I'm specifying a nested random effect correctly.

I have 773 data points, each one corresponding to a chick in a nest. I want to test for differences between half-siblings so I'm using three random effects: natal year, nest and pairID.  There are 17 natal years so year was included as a blocking factor.  There are 245 nests each with a unique number, and 177 different parent pairs, each with a unique number.  Some parent pairs have >1 nest so nest is nested within pairID.

My understanding is that in lmer (1|pairID/nest) is equivalent to (1|pairID) + (1|pairID:nest) which is equivalent to (1|pairID) + (1|nest) as long as each level of nest has a unique value, which it does. Using my dataset in a binary model I get the same results for each of the above in lmer so that's fine.

I'm just wondering whether this is the same when specifying nested random effects in MCMCglmm? I'm guessing it's not as specifying random ~ pairID + nest compared to random ~ pairID + pairID:nest in the model below gives me different significance levels for one of my main effects.  Comparing these results with the binary model in lmer suggests that I should probably be using ~ pairID + pairID:nest when using MCMCglmm but I'm not completely sure. Is this correct or should I be able to use either??

priorX1 = list(R = list(V = 1, n = 0, fix = 1), G = list(G1 = list(V = 1, n = 0.002), G2 = list(V = 1, n = 0.002)))
modelX1 <- MCMCglmm(y ~ C + D + C:D, random = ~ natalyr + pairID + pairID:nest, family = "categorical", data =early, prior = priorX1, burnin = 3000, nitt = 1003000, thin=1000)


Thanks,


Rebecca Sardell
PhD Student
Institute of Biological & Environmental Sciences
University of Aberdeen
Zoology Building
Tillydrone Avenue
Aberdeen
AB24 2TZ
Scotland



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