Hello, I have two "related" questions, one about MCMClogit and the other about BRUGS:
Given the data on nausea due to diuretic and nsaid below: nsaid diuretic yes no 0 0 185 6527 0 1 53 1444 1 0 42 1293 1 1 25 253 A logistic regression in glm gives: Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -3.56335 0.07456 -47.794 < 2e-16 *** nsaid 0.13630 0.17361 0.785 0.43242 diuretic 0.25847 0.15849 1.631 0.10293 I(nsaid * diuretic) 0.85407 0.30603 2.791 0.00526 ** But in BRUGS: model { for(i in 1:N) { yes[i] ~ dbin(p[i],no[i]) logit(p[i]) <- beta0+beta1*nsaid[i]+beta2*diuretic[i]+beta3*(nsaid[i]*diuretic[i]) } beta0 ~ dnorm(0,0.05) beta1 ~ dnorm(0,0.05) beta2 ~ dnorm(0,0.05) beta3 ~ dnorm(0,0.05) } results: > samplesStats("*") mean sd MC_error val2.5pc median val97.5pc start sample beta0 -3.5370 0.07481 0.001134 -3.68800 -3.5370 -3.3910 1001 10000 beta1 0.1332 0.17540 0.003035 -0.21610 0.1354 0.4663 1001 10000 beta2 0.2591 0.15710 0.002757 -0.05212 0.2608 0.5610 1001 10000 beta3 0.9142 0.30900 0.005573 0.30840 0.9176 1.5150 1001 10000 The interaction term beta3 (0.9142) is a little different from the one of glm, why? Using the MCMClogit (same burnin and iterations as above) from the MCMC package gives a closer estimate to glm Mean SD Naive SE Time-series SE (Intercept) -3.5612 0.07678 0.0007678 0.003306 nsaid 0.1356 0.17240 0.0017240 0.007093 diuretic 0.2453 0.16045 0.0016045 0.005340 nsaid:diuretic 0.8558 0.30756 0.0030756 0.011460 But the data cannot be entered in a summary like they are above (yes and no counts), instead they have to be entered as such: nsaid diuretic nausea 0 0 1 0 0 1 0 0 1 0 0 1 1 0 1 etc... more 9800 rows! Is there a way to use summary data (yes, no) with MCMClogit? THANKS -- View this message in context: http://www.nabble.com/Comparing-MCMClogit%2C-glm-and-BRUGS-tf3663589.html#a10236835 Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.