models/goodbad_models/assagwc.R

In soniamitchell/SpARKjags:

model {
  # Define likelihood model for data:
  for (p in 1:N_patients)
  {
    # probability of belonging to the bad group
    bad.p[p] ~ dbern(prob.of.bad.hosp)
    # index the bad group in a.prob
    index.bad.p[p] <- bad.p[p] + 1

    for(a in 1:antibiotic_classes)
    {
      # Response is different for each antibiotic and depending on
      # which pop it's from
      response[h_GUID[p],a] ~
        dbern(assagwc.prob[a,
                          index.bad.p[p],
                          sample_season[h_sample_GUID[p]],
                          age_group[h_sample_GUID[p]],
                          ward[h_sample_GUID[p]],
                          clinical[sample_type[h_sample_GUID[p]]]])
    }
  }

  for (gp in 1:N_gp)
  {
    # probability of belonging to the bad group
    bad.gp[gp] ~ dbern(prob.of.bad.gp)
    # index the bad group in a.prob
    index.bad.gp[gp] <- bad.gp[gp] + 1

    for(a in 1:antibiotic_classes)
    {
      # Response is different for each antibiotic and depending on
      # which pop it's from
      response[gp_GUID[gp],a] ~
        dbern(assag.gp.prob[a,
                            index.bad.gp[gp],
                            sample_season[gp_sample_GUID[gp]],
                            age_group[gp_sample_GUID[gp]]])
    }
  }

  for (v in 1:N_volunteers)
  {
    # probability of belonging to the bad group
    bad.v[v] ~ dbern(prob.of.bad.vol)
    # index the bad group in a.prob
    index.bad.v[v] <- bad.v[v] + 1

    for(a in 1:antibiotic_classes)
    {
      # Response is different for each antibiotic and depending on
      # which pop it's from
      response[v_GUID[v],a] ~
        dbern(assag.v.prob[a,
                           index.bad.v[v],
                           sample_season[v_sample_GUID[v]],
                           age_group[v_sample_GUID[v]]])
    }
  }

  for (o in 1:N_outpatients)
  {
    # probability of belonging to the bad group
    bad.o[o] ~ dbern(prob.of.bad.out)
    # index the bad group in a.prob
    index.bad.o[o] <- bad.o[o] + 1

    for(a in 1:antibiotic_classes)
    {
      # Response is different for each antibiotic and depending on
      # which pop it's from
      response[o_GUID[o],a] ~
        dbern(assag.o.prob[a,
                           index.bad.o[o],
                           sample_season[o_sample_GUID[o]],
                           age_group[o_sample_GUID[o]]])
    }
  }

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

  # Define the priors:
  for(a in 1:antibiotic_classes)
  {
    # probability of being resistant in the good group (less resistances)
    antibiotic.class.effect[a, 1] ~ dnorm(intercept, tau.class)
    logit(a.prob[a,1]) <- antibiotic.class.effect[a, 1]

    # probability of being resistant in the bad group (many resistances) will
    # always be higher than the good group
    antibiotic.class.effect[a, 2] ~ dnorm(intercept.plus, tau.class)
    logit(a.prob[a,2]) <- antibiotic.class.effect[a, 2]
  }

  for(s in 1:N_sample_season)
  {
    sampleseason.effect[s] ~ dnorm(0, tau.sampleseason)
  }

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

  for (w in hosp_wards)
  {
    w.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(b in 1:2)
    {
      for(s in 1:N_sample_season)
      {
        for (g in 1:N_age_group)
        {
          assag.effect[a,b,s,g] <- antibiotic.class.effect[a,b] +
            sampleseason.effect[s] + agegroup.effect[g]

          for (w in hosp_wards)
          {
            for (c in c(ncarr, nclin))
            {
              logit(assagwc.prob[a,b,s,g,w,c]) <- assag.effect[a,b,s,g] +
                w.effect[w] + clin.effect[c]
            }
          }
          # equivalent to assagw.prob
          logit(assag.gp.prob[a,b,s,g]) <- assag.effect[a,b,s,g] + gpcw.effect
          logit(assag.v.prob[a,b,s,g]) <- assag.effect[a,b,s,g] + vcw.effect
          logit(assag.o.prob[a,b,s,g]) <- assag.effect[a,b,s,g] + ocw.effect
        }
      }
    }
  }

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

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

  # Difference between distribution means of "good" and "bad" groups
  diff ~ dgamma(0.001, 0.001)
  intercept.plus <- intercept + diff

  # Probability of being in the bad group
  prob.of.bad.hosp ~ dbeta(1, 1)
  prob.of.bad.gp ~ dbeta(1, 1)
  prob.of.bad.vol ~ dbeta(1, 1)
  prob.of.bad.out ~ dbeta(1, 1)

  # Prior values for precision
  tau.class ~ dgamma(0.001, 0.001)
  tau.sampleseason ~ 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.sampleseason <- sqrt(1/tau.sampleseason)
  sd.agegroup <- sqrt(1/tau.agegroup)
  sd.ward <- sqrt(1/tau.ward)

  #monitor# full.pd, dic, deviance, a.prob, prob.of.bad.hosp, prob.of.bad.gp, prob.of.bad.vol, prob.of.bad.out, bad.p, bad.gp, bad.v, bad.o, intercept, sd.class, sd.sampleseason, sd.agegroup, sd.ward
}