model {
for (j in 1:8) {
for (i in offset[j]:offset[j+1]-1) {
d[i] <- d.com[j]; x[i] <- x.com[j]; z[i] <- z.com[j]
}
}
for (j in 9:12) {
for (i in offset[j]:offset[j+1]-1) {
d[i] <- d.inc[j-8]; x[i] <- x.inc[j-8]
}
}
for (i in 1:n) {
d[i] ~ dbern(p[i])
logit(p[i]) <- beta0 + beta*z[i]
z[i] ~ dbern(psi)
x[i] ~ dbern(phi[z1[i], d1[i]])
z1[i] <- z[i] + 1
d1[i] <- d[i] + 1
}
for (j in 1:2) {
for (k in 1:2) {
phi[j, k] ~ dunif(0, 1)
}
}
psi ~ dunif(0, 1)
beta0 ~ dnorm(0, 0.0001)
beta ~ dnorm(0, 0.0001)
}
Inits:
list(psi = 0.5, beta0 = 0, beta = 0,
phi = structure(.Data = c(0.5,0.5,0.5,0.5), .Dim = c(2,2)))
Data:
list(
n = 2044,
d.com = c(1,1,1,1,0,0,0,0),
z.com = c(0,0,1,1,0,0,1,1),
x.com = c(0,1,0,1,0,1,0,1),
d.inc = c(0,0,1,1),
x.inc = c(0,1,0,1),
offset = c(1,14,17,22,40,73,84,100,116,817,1352,1670,2045)
)
node mean sd MC error 2.5% median 97.5% start sample
beta 0.6213 0.3617 0.01924 -0.09153 0.6188 1.345 1001 20000
beta0 -0.9059 0.199 0.01045 -1.321 -0.8996 -0.5283 1001 20000
phi[1,1] 0.3177 0.05309 0.00199 0.2109 0.3186 0.4199 1001 20000
phi[1,2] 0.2212 0.08055 0.003301 0.07556 0.2188 0.3884 1001 20000
phi[2,1] 0.5691 0.06352 0.002116 0.4428 0.5683 0.6941 1001 20000
phi[2,2] 0.7638 0.06187 0.002506 0.6409 0.7646 0.8806 1001 20000
psi 0.4923 0.04304 0.001771 0.4057 0.4929 0.5771 1001 20000