In goldingn/greta: Simple and Scalable Statistical Modelling in R

Cormack-Jolly-Seber model

Cormack-Jolly-Seber (CJS) models estimate probabilities of survival and recapture from mark-recapture data. These models assume that we can only ever see individuals that have been initially marked and released or recaptured following release (i.e. individuals do not exist until first observed). The two key parameters are survival, $\phi$, and probability of recapture, $p$. There is an additional derived parameter, $\chi$, which is the probability that an individual is not recaptured following its final capture. $\chi$ marginalises over multiple scenarios in which the individual is not observed either because it has died or because it is alive but not detected.

The introductory book to the program MARK has a lot of information on mark-recapture models, including CJS models (starting in Ch. 1) and the broader class of Jolly-Seber models (Ch. 12). There is also a section on mark-recapture models in the Stan language manual, which goes through the derivation of the parameter $\chi$.

data

n_obs <- 100
n_time <- 20
y <- matrix(sample(c(0, 1), size = (n_obs * n_time), replace = TRUE),
            ncol = n_time)

greta code

# data summaries
first_obs <- apply(y, 1, function(x) min(which(x > 0)))
final_obs <- apply(y, 1, function(x) max(which(x > 0)))
obs_id <- apply(y, 1, function(x) seq(min(which(x > 0)), max(which(x > 0)), by = 1)[-1])
obs_id <- unlist(obs_id)
capture_vec <- apply(y, 1, function(x) x[min(which(x > 0)):max(which(x > 0))][-1])
capture_vec <- unlist(capture_vec)

# priors
phi <- beta(1, 1, dim = n_time)
p <- beta(1, 1, dim = n_time)

# derived parameter
chi <- ones(n_time)
for (i in seq_len(n_time - 1)) {
  tn <- n_time - i
  chi[tn] <- (1 - phi[tn]) + phi[tn] * (1 - p[tn + 1]) * chi[tn + 1]
}

# dummy variables
alive_data <- ones(length(obs_id))            # definitely alive
not_seen_last <- final_obs != 20              # ignore observations in last timestep
final_observation <- ones(sum(not_seen_last)) # final observation

# set likelihoods
distribution(alive_data) <- bernoulli(phi[obs_id - 1])
distribution(capture_vec) <- bernoulli(p[obs_id])
distribution(final_observation) <- bernoulli(chi[final_obs[not_seen_last]])

BUGS/JAGS code

model {
  # priors
  for (t in 1:(n_time - 1)) {
    phi[t] ~ dunif(0, 1)
    p[t] ~ dunif(0, 1)
  }
  # likelihood
  for (i in 1:n_obs) {
    z[i, first_obs[i]] <- 1   # state at first capture must be 1!
    for (t in (first_obs[i] + 1):n_time) {
      mu1[i, t] <- phi[t - 1] * z[i, t - 1] 
      z[i, t] ~ dbern(mu1[i, t])   # true state
      mu2[i, t] <- p[t - 1] * z[i, t]
      y[i, t] ~ dbern(mu2[i, t])      # observed state
    }
  }
}

Stan code

cat(readLines('https://raw.githubusercontent.com/stan-dev/example-models/master/misc/ecology/mark-recapture/cjs-K.stan'), sep = '\n')

goldingn/greta documentation built on May 24, 2021, 11 a.m.