From bae590d3fdf46af3ace329f99eaf0e00053a8b09 Mon Sep 17 00:00:00 2001 From: Max Joseph Date: Mon, 27 Apr 2020 21:37:23 -0600 Subject: [PATCH] Add an activity center image to the README and make the exponential encounter model a bit more streamlined --- README.md | 2 + activity-center.png | Bin 0 -> 193439 bytes ch07/exponential-encounter-fn-data-aug.R | 25 +++++++++++++ ch07/exponential-encounter-fn-s-data-aug.stan | 35 ++++++++---------- 4 files changed, 42 insertions(+), 20 deletions(-) create mode 100644 activity-center.png diff --git a/README.md b/README.md index 4bd6656..1191ae3 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,6 @@ +![](activity-center.png) + # scr-stan: spatial capture-recapture examples in Stan diff --git a/activity-center.png b/activity-center.png new file mode 100644 index 0000000000000000000000000000000000000000..c18f0369087e18bb9fba864df9f68cbaf1f3775a GIT binary patch literal 193439 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b/ch07/exponential-encounter-fn-s-data-aug.stan @@ -26,6 +26,8 @@ parameters { transformed parameters { matrix[M, n_trap] logit_p; matrix[M, 2] s = append_col(s1, s2); + vector[M] lp_if_present; + vector[M] log_lik; { matrix[M, n_trap] dist_to_trap; @@ -40,6 +42,17 @@ transformed parameters { log_p = log_inv_logit(alpha0) - alpha1 * dist_to_trap; logit_p = log_p - log1m_exp(log_p); } + + for (i in 1:M) { + lp_if_present[i] = bernoulli_lpmf(1 | psi) + + binomial_logit_lpmf(y[i, ] | n_occasion, logit_p[i, ]); + if (sum(y[i, ]) > 0) { + log_lik[i] = lp_if_present[i]; + } else { + log_lik[i] = log_sum_exp(lp_if_present[i], bernoulli_lpmf(0 | psi)); + } + } + } model { @@ -48,18 +61,7 @@ model { alpha1 ~ normal(0, 3); // likelihood - for (i in 1:M) { - if (sum(y[i, ]) > 0) { - 1 ~ bernoulli(psi); - y[i, ] ~ binomial_logit_lpmf(n_occasion, logit_p[i, ]); - } else { - target += log_sum_exp( - bernoulli_lpmf(1 | psi) - + binomial_logit_lpmf(y[i, ] | n_occasion, logit_p[i, ]), - bernoulli_lpmf(0 | psi) - ); - } - } + target += sum(log_lik); } generated quantities { @@ -72,16 +74,9 @@ generated quantities { for (i in 1:M) { if (sum(y[i, ]) > 0) { - lp_present[i] = 0; z[i] = 1; } else { - lp_present[i] = bernoulli_lpmf(1 | psi) - + binomial_logit_lpmf(y[i, ] | n_occasion, logit_p[i, ]) - - log_sum_exp( - bernoulli_lpmf(1 | psi) - + binomial_logit_lpmf(y[i, ] | n_occasion, logit_p[i, ]), - bernoulli_lpmf(0 | psi) - ); + lp_present[i] = lp_if_present[i] - log_lik[i]; z[i] = bernoulli_rng(exp(lp_present[i])); } }