I have specified the orthogonal option in discrete models based on Browne's (2012) recommendation.
In order to increase Effective Sample Sizes of the contextual variable in the contextual-effects model, I have added hierarchical centering in MCMC as follows:
Code: Select all
* Run the model
quietly runmlwin vote cons $controlVars Inteff_c Exteff_c dependency_c, level2(cntry_n: cons) level1(ind:) ///
discrete(distribution(binomial) link(logit) denom(denomb) pql2)
* Define Binomial hierarchical centering algorithm
matrix b = e(b)
matrix V = e(V)
runmlwin vote cons $controlVars Inteff_c Exteff_c dependency_c, level2(cntry_n: cons) level1(ind:) ///
discrete(distribution(binomial) link(logit) denom(denomb)) ///
mcmc(orth hcen(2) seed(1) burnin(1000) chain(10000)) or initsb(b) initsv(V) nopause nogroup
1. Is it advisable to remove orthogonal option in the discrete model when hierarchical centering is specified?
2. If I remove the orthogonal option from three out of eight discrete models, is Bayesian DIC still applicable to test hypotheses across all models?
3. In MLwiN MCMC Manual (25.4 Binomial example in practice), ESS is increasing with better convergence when the orthogonal option is specified after hierarchical centering. In my case, ESS is decreasing when orthogonal specified. What may be the reason(s) behind this?
Looking forward,
Regards