updated ch24 ARM models

Author: PeterLi2016

ARM/Ch.24/dogs.stan

@@ -24,5 +24,8 @@ transformed parameters {
 }
 model {
   beta ~ normal(0, 100);
-  y ~ bernoulli_logit(p);
+  for (i in 1:n_dogs) {
+    for (j in 1:n_trials)
+      y[i,j] ~ bernoulli_logit(p[i,j]);
+  }
 }

ARM/Ch.24/dogs_check.stan

@@ -1,7 +1,7 @@
 data {
   int<lower=0> n_dogs;
   int<lower=0> n_trials;
-  matrix[n_dogs,n_trials] y;
+  int<lower=0, upper=1> y[n_dogs,n_trials];
 }
 parameters {
   real<lower=0,upper=100> sigma_b1;
@@ -10,41 +10,39 @@ parameters {
   real<lower=-1,upper=1> rho_b;
   vector[2] mu_beta;
 }
-transformed parameters {
+model {
   vector[n_dogs] beta1;
   vector[n_dogs] beta2;
   matrix[n_dogs,n_trials] n_avoid;
   matrix[n_dogs,n_trials] n_shock;
   matrix[n_dogs,n_trials] p;
-  cov_matrix[2] Sigma_b;
+  matrix[2,2] Sigma_b;
+
+  sigma_b1 ~ uniform (0, 100);               
+  sigma_b2 ~ uniform (0, 100);                  
+  rho_b ~ uniform(-1, 1);
+  mu_beta ~ normal(0, 100);
 
   Sigma_b[1,1] <- pow(sigma_b1,2);
   Sigma_b[2,2] <- pow(sigma_b2,2);
   Sigma_b[1,2] <- rho_b*sigma_b1*sigma_b2;
   Sigma_b[2,1] <- Sigma_b[1,2];  
 
+  for (i in 1:n_dogs)
+    transpose(beta_neg[i]) ~ multi_normal_prec(mu_beta,Sigma_b);
 
   for (j in 1:n_dogs) {
     n_avoid[j,1] <- 0;
     n_shock[j,1] <- 0;
-    beta1[j] <- -exp(b_neg[j,1]);
-    beta2[j] <- -exp(b_neg[j,2]);
+    beta1[j] <- -exp(beta_neg[j,1]);
+    beta2[j] <- -exp(beta_neg[j,2]);
     for (t in 2:n_trials) {
       n_avoid[j,t] <- n_avoid[j,t-1] + 1 - y[j,t-1];
       n_shock[j,t] <- n_shock[j,t-1] + y[j,t-1];
     }
-    for (t in 1:n_trials)
-      p[j,t] <- exp(beta1[j] * n_avoid[j,t] + beta2[j] * n_shock[j,t]);
+    for (t in 1:n_trials) {
+      p[j,t] <- inv_logit(beta1[j] * n_avoid[j,t] + beta2[j] * n_shock[j,t]);
+      y[j,t] ~ bernoulli(p[j,t]);
+    }
   }
 }
-model {
-  sigma_b1 ~ uniform (0, 100);               
-  sigma_b2 ~ uniform (0, 100);                  
-  rho_b ~ uniform(-1, 1);
-  mu_beta ~ normal(0, 100);
-
-  for (i in 1:n_dogs)
-    transpose(b_neg[i]) ~ multi_normal_prec(mu_beta,Sigma_b);
-
-  y ~ binomial(p, 1);
-}

ARM/Ch.24/dogs_log.stan

@@ -1,7 +1,7 @@
 data {
   int<lower=0> n_trials;
   int<lower=0> n_dogs;
-  matrix[n_dogs,n_trials] y;
+  int<lower=0,upper=1> y[n_dogs,n_trials];
 }
 parameters {
   vector[2] beta;
@@ -19,11 +19,14 @@ transformed parameters {
       n_shock[j,t] <- n_shock[j,t-1] + y[j,t-1];
     }
     for (t in 1:n_trials)
-      p[j,t] <- exp(beta[1] * n_avoid[j,t] + beta[2] * n_shock[j,t]);
+      p[j,t] <- inv_logit(beta[1] * n_avoid[j,t] + beta[2] * n_shock[j,t]);
   }
 }
 model {
   beta[1] ~ uniform(-100, 0);
   beta[2] ~ uniform(0, 100);
-  y ~ binomial(p, 1);
+  for (i in 1:n_dogs) {
+    for (j in 1:n_trials)
+      y[i,j] ~ bernoulli(p[i,j]);
+  }
 }