Hi everyone,
I'm running a bayesian regression using the package MCMCglmm (Hadfield 2010)
and to reach a normal posterior distribution of estimates, I increased the
number of iteration as well as the burnin threshold. However, it had unexpected
outcomes. Although it improved posterior distribution, it also increased
dramatically the value of estimates and decrease DIC.
Here an example:
>head(spring)
pres large_road small_road cab0 2011 32
781 102 179 2040 1256
654 9841 187 986 7560
21 438 571 13
5 439
>#pres is presence/absence data and other variable are distance to these
>features
>## with 200,000 iteration and 30,000 burnin>prior <- list(R = list(V = 1,
>nu=0.002))>sp.simple <- MCMCglmm(pres ~ large_road + cab + small_road, family
>= "categorical", nitt = 200000, thin = 200, burnin = 30000,
> data = spring, prior = prior, verbose = FALSE, pr =
>TRUE)
>summary(sp.simple)
Iterations = 30001:199801 Thinning interval = 200 Sample size = 850
DIC: 14045.31
R-structure: ~units
post.mean l-95% CI u-95% CI eff.sampunits 294.7
1.621 621.9 1.982
Location effects: pres ~ large + cab + small + Coupe_0_5 + Regeneration +
Res_mature + DH + Autre + Eau + Pert_nonregen + MF + Coupe_6_20
post.mean l-95% CI u-95% CI eff.samp
pMCMC (Intercept) 5.76781 0.77622 9.24375 1.829
<0.001 **large 0.37487 0.02692 0.75282
3.310 <0.001 **cab 0.94639 0.09906 1.57939
2.096 <0.001 **small -1.62192 -2.60873
-0.20191 2.002 <0.001 **
>## with 1,000,000 iteration and 500,000 burnin>prior <- list(R = list(V = 1,
>nu=0.002))>sp.simple <- MCMCglmm(pres ~ large_road + cab + small_road, family
>= "categorical", nitt = 1000000, thin = 200, burnin = 500000,
> data = spring, prior = prior, verbose = FALSE, pr =
>TRUE)
>summary(sp.simple)
Iterations = 500001:999801 Thinning interval = 200 Sample size = 2500
DIC: 858.6316
R-structure: ~units
post.mean l-95% CI u-95% CI eff.sampunits 26764
17548 34226 124.5
Location effects: pres ~ large_road + cab + small_road
post.mean l-95% CI u-95% CI eff.samp pMCMC
(Intercept) 60.033 47.360 70.042 137.9 <4e-04
***large_road 3.977 1.279 6.616 1484.6
0.0080 ** cab_road 9.913 6.761 13.020 333.7
<4e-04 ***small -16.945 -20.694 -13.492 194.9
<4e-04 ***
I'm then wandering if it is because more iteration produce better estimates and
then a model that had a better fit with the data.
Anyone can help me?
Rémi
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