bayesMeasurementError/cox_reg.bug at master · jwb133/bayesMeasurementError · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
data
{
for(i in 1:N) {
  for(j in 1:T) {
    Y[i,j] <- step(obs.t[i]-t[j]+eps)
    dN[i,j] <- Y[i,j]*step(t[j+1]-obs.t[i]-eps)*fail[i]
  }
  out1[i] <- 0
  cumSumIndex[i] <- sum(Y[i,])
}
}


model
{

dL0[1] ~ dgamma(muhaz[1],c)
cumHaz[1] <- 0
muhaz[1] <- dL0.star[1]*c
dL0.star[1] <- r*(t[2]-t[1])
for(j in 2:T) {
  dL0[j] ~ dgamma(muhaz[j],c)
  cumHaz[j] <- cumHaz[j-1] + dL0[j-1]
  muhaz[j] <- dL0.star[j]*c
  dL0.star[j] <- r*(t[j+1]-t[j])
}
cumHaz[T+1] <- cumHaz[T] + dL0[T]

for(i in 1:N) {
  out1[i] ~ dpois(cumHaz[cumSumIndex[i]]*exp(mu[i]))
  fail[i] ~ dpois(dL0[cumSumIndex[i]]*exp(mu[i]))

  mu[i]<-beta[1]*x[i]+beta[2]*z[i]
  x[i]~dnorm(mux[i],taux)
  mux[i]<-gamma[1]+gamma[2]*z[i]
  w1[i]~dnorm(x[i],tauu)
}
for(i in 1:N1) {
  w2[i]~dnorm(x[i],tauu)
}

beta ~ dmnorm(betamean,betaprec)

tauu~dgamma(tauu_alpha,tauu_beta)
sigmau<-pow(tauu,-0.5)

gamma~dmnorm(gammamean,gammaprec)

taux~dgamma(taux_alpha,taux_beta)
sigmax<-pow(taux,-0.5)

}