[DATALAD] Recorded changes

code/models/blocks/confusion_inverse_model_binomial_hurdle.stan

@@ -9,7 +9,7 @@ real inverse_model_lpdf(array [] matrix actual_confusion,
     //array [] vector actual_fp_rate,
     matrix mus,
     matrix etas,
-    vector p//,
+    matrix p//,
     //vector mus_fp,
     //vector alphas_fp
     ) {
@@ -26,8 +26,8 @@ real inverse_model_lpdf(array [] matrix actual_confusion,
                 ll += beta_proportion_lpdf(actual_confusion[k-start+1,i] | mus[i,:], etas[i,:]);
                 //ll += gamma_lpdf(actual_fp_rate[k] | alphas_fp, alphas_fp./mus_fp);
                 
-                expect[i] = dot_product(truth_vocs[k,:], (2-p).*actual_confusion[k-start+1,:,i]);
-                sd[i] = dot_product(truth_vocs[k,:], (2-p).*(actual_confusion[k-start+1,:,i].*(1-actual_confusion[k-start+1,:,i])));
+                expect[i] = dot_product(truth_vocs[k,:], (2-p[:,i]).*actual_confusion[k-start+1,:,i]);
+                sd[i] = dot_product(truth_vocs[k,:], (2-p[:,i]).*(actual_confusion[k-start+1,:,i].*(1-actual_confusion[k-start+1,:,i])));
                 //expect[i] += actual_fp_rate[k,i] * duration;
             }
             

code/models/blocks/confusion_model_binomial_hurdle_fast.stan

@@ -7,7 +7,7 @@ real confusion_model_lpdf(array[] matrix lambda,
     array[] int group,
     array[] real age,
     array[] real clip_duration,
-    vector p
+    matrix p
 ) {
     real ll = 0;
     vector [4] dp;
@@ -34,19 +34,19 @@ real confusion_model_lpdf(array[] matrix lambda,
                     // (as opposed to two vocs)
                     for (chi1 in max(0, 2*chi_d-algo[k,i]):chi_d) {
                         int chi = 2*chi_d-chi1;
-                        bp[1] = chi_d==0?0:binomial_lpmf(chi1 | chi_d, p[1]);
+                        bp[1] = chi_d==0?0:binomial_lpmf(chi1 | chi_d, p[1,i]);
 
                         for (och_d in 0:(truth[k,2]>0?min(truth[k,2], algo[k,i]-chi):0)) {
                             dp[2] = truth[k,2]==0?0:binomial_lpmf(och_d | truth[k,2], lambda[lg,2,i]);
                             for (och1 in max(0, 2*och_d-(algo[k,i]-chi)):och_d) {
                                 int och = 2*och_d-och1;
-                                bp[2] = och_d==0?0:binomial_lpmf(och1 | och_d, p[2]);
+                                bp[2] = och_d==0?0:binomial_lpmf(och1 | och_d, p[2,i]);
 
                                 for (fem_d in 0:(truth[k,3]>0?min(truth[k,3], algo[k,i]-chi-och):0)) {
                                     dp[3] = truth[k,3]==0?0:binomial_lpmf(fem_d | truth[k,3], lambda[lg,3,i]);
                                     for (fem1 in max(0, 2*fem_d-(algo[k,i]-chi-och)):fem_d) {
                                         int fem = 2*fem_d-fem1;
-                                        bp[3] = fem_d==0?0:binomial_lpmf(fem1 | fem_d, p[3]);
+                                        bp[3] = fem_d==0?0:binomial_lpmf(fem1 | fem_d, p[3,i]);
 
                                         for (mal_d in 0:(truth[k,4]>0?min(truth[k,4], algo[k,i]-chi-och-fem):0)) {
                                             dp[4] = truth[k,4]==0?0:binomial_lpmf(mal_d | truth[k,4], lambda[lg,4,i]);
@@ -54,7 +54,7 @@ real confusion_model_lpdf(array[] matrix lambda,
                                                 int mal = 2*mal_d-mal1;
                                                 int delta = algo[k,i] - (mal+fem+och+chi);
                                                 if (delta==0) {
-                                                    bp[4] = mal_d==0?0:binomial_lpmf(mal1 | mal_d, p[4]);
+                                                    bp[4] = mal_d==0?0:binomial_lpmf(mal1 | mal_d, p[4,i]);
                                                     log_contrib_comb[n] += sum(dp+bp);
                                                     n = n+1;
                                                 }

code/models/blocks/confusion_model_parameters_binomial_hurdle.stan

@@ -1,4 +1,4 @@
 matrix<lower=1>[n_classes,n_classes] etas;
 matrix<lower=0,upper=1>[n_classes,n_classes] mus;
 array [n_groups] matrix<lower=0,upper=1>[n_classes,n_classes] lambda;
-vector<lower=0,upper=1>[n_classes] p;
+matrix<lower=0,upper=1>[n_classes,n_classes] p;

code/models/blocks/confusion_model_priors_binomial_hurdle.stan

@@ -1,6 +1,5 @@
-p ~ beta(3,1);
-
 for (i in 1:n_classes) {
+    p[i] ~ beta(3,1);
     for (j in 1:n_classes) {
         mus[i,j] ~ uniform(0, 1);
         etas[i,j] ~ pareto(1, 1.5);