MAPR_Transience_MultiEvent_ptime_constemig.jags

    model {
    
    # -------------------------------------------------
    # Parameters:
    # phi: survival probability for adults
    # p: recapture probability when breeding
    # -------------------------------------------------

    # Priors and constraints
    for (t in 1:(n.occasions-1)){
      #logit(phi[t]) <- mu.phi + surv.raneff[t]
      #surv.raneff[t] ~ dnorm(0, tau.phi)
      p[t] ~ dunif(0, 1)
      #emigrate[t] ~ dunif(0,0.25)
    }

    mean.phi ~ dunif(0, 1)             # Prior for mean survival
    #mean.p ~ dunif(0, 1)             # Prior for mean recapture
    #mu.phi <- log(mean.phi / (1-mean.phi)) # Logit transformation
    #sigma.phi ~ dunif(0, 5)                # Prior for standard deviation
    #tau.phi <- pow(sigma.phi, -2)
    emigrate ~ dunif(0,0.25)

    # States (S):
    # 1 dead
    # 2 alive in Prion Cave
    # 3 alive as transient
    
    # Observations (O):
    # 1 observed
    # 2 not observed
    # -------------------------------------------------
    

    
    # -------------------------------------------------
    # Define state-transition and observation matrices 
    # -------------------------------------------------
    
    for (i in 1:nind){
    
    for (t in f[i]:(n.occasions-1)){
    
    # Define probabilities of state S(t+1) [last dim] given S(t) [first dim]
    
    ps[1,i,t,1]<-1    ## dead birds stay dead
    ps[1,i,t,2]<-0
    ps[1,i,t,3]<-0
    
    ps[2,i,t,1]<-(1-mean.phi)
    ps[2,i,t,2]<-mean.phi*(1-emigrate)
    ps[2,i,t,3]<-mean.phi*emigrate
    
    ps[3,i,t,1]<-(1-mean.phi)
    ps[3,i,t,2]<-0
    ps[3,i,t,3]<-mean.phi
    
    # Define probabilities of O(t) [last dim] given S(t)  [first dim]
    
    po[1,i,t,1]<-0
    po[1,i,t,2]<-1
    
    po[2,i,t,1]<-p[t]
    po[2,i,t,2]<-(1-p[t])
    
    po[3,i,t,1]<-0
    po[3,i,t,2]<-1
    
    } #t
    } #i


    # Likelihood 
    for (i in 1:nind){
      # Define latent state at first capture
      z[i,f[i]] <- 2 ## alive when first marked
      for (t in (f[i]+1):n.occasions){
        # State process: draw S(t) given S(t-1)
        z[i,t] ~ dcat(ps[z[i,t-1], i, t-1,])
        # Observation process: draw O(t) given S(t)
        y[i,t] ~ dcat(po[z[i,t], i, t-1,])
      } #t
    } #i


    }