models/full_models/asmaggwc.R

In soniamitchell/SpARKjags:

model {
  # Define likelihood model for data:
  for (p in 1:N_patients)
  {
    for(a in 1:antibiotic_classes)
    {
      response[h_GUID[p],a] ~
        dbern(asmaggwc.prob[a,
                            sample_month[h_sample_GUID[p]],
                            age_group[h_sample_GUID[p]],
                            gender[h_sample_GUID[p]],
                            ward[h_sample_GUID[p]],
                            clinical[sample_type[h_sample_GUID[p]]]])
    }
  }

  for (gp in 1:N_gp)
  {
    for(a in 1:antibiotic_classes)
    {
      response[gp_GUID[gp],a] ~
        dbern(gp.prob[a,
                      sample_month[gp_sample_GUID[gp]],
                      age_group[gp_sample_GUID[gp]],
                      gender[gp_sample_GUID[gp]]])
    }
  }

  for (v in 1:N_volunteers)
  {
    for(a in 1:antibiotic_classes)
    {
      response[v_GUID[v],a] ~
        dbern(v.prob[a,
                     sample_month[v_sample_GUID[v]],
                     age_group[v_sample_GUID[v]],
                     gender[v_sample_GUID[v]]])
    }
  }

  for (o in 1:N_outpatients)
  {
    for(a in 1:antibiotic_classes)
    {
      response[o_GUID[o],a] ~
        dbern(o.prob[a,
                     sample_month[o_sample_GUID[o]],
                     age_group[o_sample_GUID[o]],
                     gender[o_sample_GUID[o]]])
    }
  }

  # ------------------------

  # Define the priors:
  for(a in 1:antibiotic_classes)
  {
    antibiotic.class.effect[a] ~ dnorm(intercept, tau.class)
    logit(a.prob[a]) <- antibiotic.class.effect[a]
  }

  for(m in 1:N_sample_month)
  {
    samplemonth.effect[m] ~ dnorm(0, tau.samplemonth)
  }

  for (g in 1:N_age_group)
  {
    agegroup.effect[g] ~ dnorm(0, tau.agegroup)
    logit(agegroup.prob[g]) <- agegroup.effect[g]
  }

  gender.effect[female] ~ dnorm(0, 0.001)
  gender.effect[male] <- -gender.effect[female]

  for (w in hosp_wards)
  {
    ward.effect[w] ~ dnorm(0, tau.ward)
  }

  # equivalent to clin.effect + ward.effect
  gpcw.effect ~ dnorm(gp.effect, tau.ward)
  vcw.effect ~ dnorm(gp.effect, tau.ward)
  ocw.effect ~ dnorm(gp.effect, tau.ward)

  clin.effect[ncarr] ~ dnorm(0, 0.001)
  clin.effect[nclin] <- -clin.effect[ncarr]

  gp.effect <- clin.effect[gp_clinical]
  o.effect  <- clin.effect[o_clinical]
  v.effect  <- clin.effect[v_clinical]

  # ------------------------

  for(a in 1:antibiotic_classes)
  {
    for(m in 1:N_sample_month)
    {
      for (r in 1:N_age_group)
      {
        for (g in genders)
        {
          asmagg.effect[a,m,r,g] <- antibiotic.class.effect[a] +
            samplemonth.effect[m] + agegroup.effect[r] + gender.effect[g]

          for(w in hosp_wards)
          {
            for (c in c(ncarr, nclin))
            {
              logit(asmaggwc.prob[a,m,r,g,w,c]) <- asmagg.effect[a,m,r,g] +
                ward.effect[w] + clin.effect[c]
            }
          }

          logit(gp.prob[a,m,r,g]) <- asmagg.effect[a,m,r,g] + gpcw.effect
          logit(v.prob[a,m,r,g]) <- asmagg.effect[a,m,r,g] + vcw.effect
          logit(o.prob[a,m,r,g]) <- asmagg.effect[a,m,r,g] + ocw.effect
        }
      }
    }
  }

  # Prior value for intercept (log-odds of the average resistance in all samples)
  intercept ~ dnorm(0, 0.001)

  # Prior values for precision
  tau.class ~ dgamma(0.001, 0.001)
  tau.samplemonth ~ dgamma(0.001, 0.001)
  tau.agegroup ~ dgamma(0.001, 0.001)
  tau.ward ~ dgamma(0.001, 0.001)

  # Convert precisions to sd
  sd.class <- sqrt(1/tau.class)
  sd.samplemonth <- sqrt(1/tau.samplemonth)
  sd.agegroup <- sqrt(1/tau.agegroup)
  sd.ward <- sqrt(1/tau.ward)

  #monitor# full.pd, dic, deviance, a.prob, intercept, sd.class, sd.samplemonth, sd.agegroup, sd.ward
}