Residuals for a JAGS logistic regression model

I have the following simple logistic regression model:

for (i in 1:length(target)) {

target[i] ~ dbern(p[i])

logit(p[i]) = int + b[1] * age[i] + b[2] * sex[i] + ...

}

int ~ dnorm(0.0, 1.0/0.01)

for (j in 1:13) {
b[j] ~ dnorm(0.0, 1.0/0.01)
}

} "


and would like to check the residuals, if possible at all in this case, but conceptually I do not understand, how to do this. My first thought for getting the predicted values was to calculate the logit(p(i)) values from the means of the b values taken from the samples of the above model, and than applying a bern(...) function to it would give me the predicted target values. This does not seem right to me, as I would also need to take some kind of mean from the bernoulli distribution. I am a bit confused here. What is the solution here?

Thanks!