# JAGS: Posterior Predictive Check for a Logistic Regression Model

I want to perform a posterior predictive check on some simple logistic regression models that I fitted in JAGS. I found a function in the R package jagsUI called pp.check (see doc here: (pp.check function) with an example that will calculate the Bayesian P value from the sum of residuals of the actual data and simulated data. The example is for a linear regression, so I am trying to extend it to my logistic regression model. Below I have my code that I am trying, but am not sure I'm calculating the sum of residuals correctly? Could someone please check it?

    # Logistic regression:
logistic_model <- "model{

# Likelihood

for(i in 1:n){
Y[i] ~ dbin(q[i], 1)
logit(q[i]) <- beta[1] + beta[2]*X[i,1] + beta[3]*X[i,2] +
beta[4]*X[i,3] + beta[5]*X[i,4] + beta[6]*X[i,5]

#calculate the residuals
res[i] <- Y[i] - q[i]
emp.new[i] ~ dbin(q[i], 1)
res.new[i] <- emp.new[i] - q[i]
}

#Priors

for(j in 1:6){
beta[j] ~ dnorm(0,0.1)
}
#Derived parameters
fit <- sum(regression_residual[])
fit.new <- sum(res.new[])
}"


If fit and fit.new are valid, can I then use pp.check as described in the function's documentation example?