model {
# Define likelihood model for data:
# Carbapenem resistance in hospital (gp, volunteer, and outpatient) samples
# is Bernoulli distributed with probability wc.prob (gp.prob, v.prob,
# and o.prob)
for (p in 1:N_patients)
{
h_resist[p] ~ dbern(wtc.prob[ward_type[ward[h_sample_GUID[p]]],
clinical[sample_type[h_sample_GUID[p]]]])
}
for (gp in 1:N_gp)
{
gp_resist[gp] ~ dbern(gp.prob)
}
for (v in 1:N_volunteers)
{
v_resist[v] ~ dbern(v.prob)
}
for (o in 1:N_outpatients)
{
o_resist[o] ~ dbern(o.prob)
}
# ------------------------
# Define the priors:
# Prior distribution for clin.effect (log-odds for each clinical class).
# Sample different clin.effect from normal distribution for each clinical
# class and convert to a probability). Since there is only one response
# variable, with only two levels, set intercept to 0.
#
# Prior distribution for cwt.effect (log-odds for each ward type in each
# clinical class). Sample different cw.effect from normal distribution (with
# mean clin.effect) for each ward type in each clinical class and convert
# to a probability.
clin.effect[ncarr] ~ dnorm(0, 0.001)
clin.effect[nclin] <- -clin.effect[ncarr]
# two level hierarchical model
for (c in c(ncarr, nclin)) # 1, 2!
{
for (wt in hosp_wardtypes)
{
# clinical effect is different for each ward type
cwt.effect[wt,c] ~ dnorm(clin.effect[c], tau)
logit(wtc.prob[wt,c]) <- cwt.effect[wt,c]
}
}
# equivalent to cwt.effect
gp.effect ~ dnorm(clin.effect[gp_clinical], tau)
logit(gp.prob) <- gp.effect
v.effect ~ dnorm(clin.effect[v_clinical], tau)
logit(v.prob) <- v.effect
o.effect ~ dnorm(clin.effect[o_clinical], tau)
logit(o.prob) <- o.effect
# ------------------------
# Prior values for precision
tau ~ dgamma(0.001, 0.001)
# Convert precisions to sd
sd <- 1/sqrt(tau)
# Calculate odds
c.diff <- clin.effect[ncarr] - clin.effect[nclin]
odds.c <- exp(c.diff)
#monitor# full.pd, dic, deviance, clin.effect, gp.prob, v.prob, o.prob, odds.c, sd
}
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