[R] MCMCglmm and iteration

[Rémi Lesmerises]

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


	[[alternative HTML version deleted]]