roc curve for bayesian logistic regression

Question

Is there anyone can help me implement a ROC curve for a bayesian logistic regression? been trying DPpackage but is it me or it just doesn't work.

the two models i want to compare using ROC Curve are showed below:

bayes_mod=MCMClogit(Default ~ ACTIVITY + CIF + MAN + STA + PIA + COL + CurrLiq + DebtCov + GDPgr, data=mydata, burnin=500000,mcmc=10000, tune=0.6,b0=coef(mylogit.reduced),B0=information2, subset=c(-1772,-2064,-655))


bayes_mod1=MCMClogit(Default ~ ACTIVITY + CIF + MAN + STA + PIA + COL + CurrLiq + DebtCov + GDPgr, data=mydata, burnin=500000,mcmc=10000,tune=0.6,subset=c(-1772,-2064,-655))

where Default ~ ACTIVITY + CIF + MAN + STA + PIA + COL + CurrLiq + DebtCov + GDPgr are my arguments; mydata is the database; mylogit.reduced is a logistic regression estimated prior to bayesian, B0 is the covariation matrix, and subset=c are the eliminated observations.

Answer 1

I don't know this package, but it probably provides a predict function (actually it does, I just can't find if it does for MCMClogit models as I can't find the doc for this function). You can then pass it to a ROC function like pROC:

library(pROC)
predictions <- predict(mydata, newdata=mytestdata)
roc(mytestdata$Default, predictions)