[R-sig-ME] Massive difference in random effect's posterior mean between chains
Ronan James Osullivan
113499328 @end|ng |rom um@||@ucc@|e
Wed Nov 13 19:25:30 CET 2019
Hi all,
Apologies if this question is trivial or if I'm over-looking something
obvious.
I am trying to investigate the effect of Genetic Type (A or B) and climate
on the lifetime reproductive success (LRS) of a wild-spawning fish species.
I am interacting Genetic Type with seven different climatic variables. I
have included a nested random effects structure to account for
pseudoreplication among years (~Year_of_Spawning), and to account for some
of the fish spawning across multiple years (~Year_ID2). I also interact
Genetic_Type with the relative survival of B compared to A to correct for a
survival bias between the two genetic types (they are sampled at different
points in the spawning season).
The model is specified in MCMCglmm as follows:
Model_a<- MCMCglmm(LRS ~
(Genetic_Type-1)*climate1+
(Genetic_Type -1)* climate2+
(Genetic_Type -1)* climate3+
(Genetic_Type -1)* climate4+
(Genetic_Type -1)* climate5+
(Genetic_Type -1)* climate6+
(Genetic_Type -1)* climate7+
Genetic_Type *relative_survival,
random = ~Year_of_Spawning+ Year_ID2,
family = "poisson",
data = data1,
prior = prior.exp_1,
nitt = 110000,
burnin = 10000,
thin = 100)
The above model was run to illustrate the problem, hence the low number of
iterations.
The parameter expanded prior is as follows:
prior.exp_1<- list(R = list(V = 1, nu = 2),
G = list(G1 = list(V = 1, nu = 0.002, alpha.mu = 0, alpha.V = 1000),
G2 = list(V = 1, nu = 0.002, alpha.mu = 0, alpha.V = 1000)))
If I run 2 chains of the model, then the results for the fixed effects are
similar, allowing for Monte Carlo error. However, the posterior mean
estimate for the 'Year_of_Spawning' random effect changes massively, with
the posterior mean larger than the upper 95% credible interval:
MODEL 1:
G-structure: ~Year_of_Spawning
post.mean l-95% CI u-95% CI
eff.samp
Year_of_Spawning 5.49 7.832e-05 15.85 1000
~Year_ID2
post.mean l-95% CI u-95% CI
eff.samp
Year_ID2 0.5262 0.000721 0.9471
87.03
R-structure: ~units
post.mean l-95% CI u-95% CI
eff.samp
units 0.5792 0.1383 1.082
87.61
MODEL 2:
G-structure: ~Year_of_Spawning
post.mean l-95% CI u-95% CI
eff.samp
Year_of_Spawning 39.38 9.65e-05 15.52 1000
~Year_ID2
post.mean l-95% CI u-95% CI
eff.samp
Year_ID2 0.4724 4.8e-06 0.9168
81.07
R-structure: ~units
post.mean l-95% CI u-95% CI
eff.samp
units 0.6403 0.1838 1.126
92.06
I imagine that the problem exists in my random effects structure but I am
unsure of where the error lies. Any and all help is massively appreciated.
Cheers,
Ronan
--
Ronan O'Sullivan | Ph.D student | School of Biological, Earth and
Environmental Sciences, University College Cork, Ireland |
http://fisheye.ucc.ie/toms-team/
EMPSEB 26 - Chair of Organizing Committee
Irish Ecological Association - Ordinary Committee Member
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