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Distance sampling model with binned data, translated from Sec. 8.3.1 of "Advanced Hierarchical Modeling in Ecology, vol.1"
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data { | |
int<lower = 1> N_ind; // Number of individuals | |
int<lower = 1> N_z; // Number of augment observed data | |
int<lower = 0, upper = 1> Y[N_ind + N_z]; // Augumented inds. have y=0 by | |
// definition | |
real B; // Strip half-width | |
// Larger than max observed distance | |
int<lower = 1> N_D; // N intervals | |
int<lower = 1, upper = N_D> D_class[N_ind]; // Distance class | |
real<lower = 0, upper = B> Delta; // Width of distance bins | |
vector<lower = 0, upper = B>[N_D] Midpt; // Interval mid-points | |
} | |
transformed data { | |
simplex[N_D] pi = rep_vector(Delta / B, N_D); // Prob. of x in each interval | |
} | |
parameters { | |
real<lower = 0> sigma; // Half-normal scale | |
real<lower = 0, upper = 1> psi; // DA parameter | |
} | |
transformed parameters { | |
vector<lower = 0, upper = 1>[N_D] p = exp(-(Midpt .* Midpt) / (2 * square(sigma))); | |
} | |
model { | |
sigma ~ normal(0, 1000); | |
// Y == 1 | |
for (i in 1:N_ind) { | |
target += bernoulli_lpmf(1 | psi) | |
+ bernoulli_lpmf(1 | p[D_class[i]]) | |
+ categorical_lpmf(D_class[i] | pi); | |
} | |
// Y == 0 | |
for (i in (N_ind + 1):(N_ind + N_z)) { | |
real lp[N_D + 1]; | |
lp[1] = bernoulli_lpmf(0 | psi); | |
for (g in 1:N_D) { | |
lp[g + 1] = bernoulli_lpmf(1 | psi) | |
+ bernoulli_lpmf(0 | p[g]) | |
+ categorical_lpmf(g | pi); | |
} | |
target += log_sum_exp(lp); | |
} | |
} | |
generated quantities { | |
int<lower = 0, upper = N_ind + N_z> N = N_ind; | |
real D; | |
for (i in (N_ind + 1):(N_ind + N_z)) { | |
real lp; | |
real lp2[N_D]; | |
for (g in 1:N_D) { | |
lp2[g] = bernoulli_lpmf(0 | p[g]) + categorical_lpmf(g | pi); | |
} | |
lp = bernoulli_lpmf(1 | psi) + log_sum_exp(lp2) | |
- log_sum_exp(bernoulli_lpmf(1 | psi) + log_sum_exp(lp2), | |
bernoulli_lpmf(0 | psi)); | |
N = N + bernoulli_rng(exp(lp)); | |
} | |
D = N / 60.0; | |
} |
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