MCMClogit confusion

Could anybody explain to me why

simulatedCase <- rbinom(100,1,0.5)
simDf <- data.frame(CASE = simulatedCase)
posterior_m0 <<- MCMClogit(CASE ~ 1, data = simDf, b0 = 0, B0 = 1)

always results in a MCMC acceptance ratio of 0? Any explanation would be greatly app开发者_如何学编程reciated!

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I think your problem is the model formula, since logistic regression models have no error term. Thus you model CASE ~ 1 should be replaced by something like CASE ~ x (the predictor variable x is mandatory). Here is your example, modified:

CASE <- rbinom(100,1,0.5)
x <- 1:100
posterior_m0 <- MCMClogit (CASE ~ x, b0 = 0, B0 = 1)
classic_m0 <- glm (CASE ~ x,  family=binomial(link="logit"), na.action=na.pass)

So I think your problem is not related to the MCMCpack library (disclaimer: I have never used this package).

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For anyone stumbling into this same problem :

It seems that the MCMClogit function cannot handle anything but B0=0 if your model only has an intercept.

If you add a covariate, then you can specify a precision just fine.

I would consider other packages (such as arm or rjags) if you really want to sample from this model. For a list of options available for Bayesian regression, see http://cran.r-project.org/web/views/Bayesian.html