Merge pull request #201 from drbenvincent/dev

beta-delta fixes
drbenvincent · Aug 19, 2017 · 46f737f · 46f737f
2 parents bf133f1 + 9e5815a
commit 46f737f
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Showing 5 changed files with 16 additions and 14 deletions.
diff --git a/ddToolbox/models/parametric_models/beta_delta_models/ModelMixedBetaDelta.m b/ddToolbox/models/parametric_models/beta_delta_models/ModelMixedBetaDelta.m
@@ -19,6 +19,8 @@
 			% Generate initial values of the root nodes
 			nExperimentFiles = obj.data.getNExperimentFiles();
 			for chain = 1:nchains
+				initialParams(chain).beta 	= unifrnd(0, 1, [nExperimentFiles-1,1]);
+				initialParams(chain).delta 	= normrnd(1, 0.5, [nExperimentFiles-1,1]);
 				initialParams(chain).groupW             = rand;
 				initialParams(chain).groupALPHAmu		= rand*100;
 				initialParams(chain).groupALPHAsigma	= rand*100;

diff --git a/ddToolbox/models/parametric_models/beta_delta_models/ModelSeparateBetaDelta.m b/ddToolbox/models/parametric_models/beta_delta_models/ModelSeparateBetaDelta.m
@@ -18,7 +18,7 @@
             % Generate initial values of the root nodes
 			nExperimentFiles = obj.data.getNExperimentFiles();
 			for chain = 1:nchains
-                initialParams(chain).beta = abs(normrnd(0.84, 2, [nExperimentFiles,1]));
+                initialParams(chain).beta = unifrnd(0, 1, [nExperimentFiles,1]);
 				initialParams(chain).delta = normrnd(1, 0.5, [nExperimentFiles,1]);
 				initialParams(chain).epsilon = 0.1 + rand([nExperimentFiles,1])/10;
 				initialParams(chain).alpha = abs(normrnd(0.01,10,[nExperimentFiles,1]));

diff --git a/ddToolbox/models/parametric_models/beta_delta_models/hierarchicalBetaDelta.jags b/ddToolbox/models/parametric_models/beta_delta_models/hierarchicalBetaDelta.jags
@@ -6,23 +6,17 @@ model{
 # DISCOUNT FUNCTION PARAMETERS =================================================
 # RANDOM (BY PARTICIPANT) FACTORS; HYPER-PRIORS = NO
 
+# delta is slope
 group_delta_MEAN      <- 0.9995
 group_delta_PRECISION <- 1/(0.0009^2)
 
+# beta is intercept
 group_beta_MEAN      <- 0.84
 group_beta_PRECISION <- 1/(0.11^2)
 
 for (p in 1:nRealExperimentFiles){
-
-	# get some instability if we use the reparameterisation, so we'll avoid this for the moment
-	#delta_offset[p]    ~ dnorm(0,1)
-	#delta[p] <- group_delta_MEAN + delta_offset[p] * group_delta_PRECISION
 	delta[p] ~ dnorm(group_delta_MEAN, group_delta_PRECISION)
-
-	# using reparameterisation to avoid funnel of hell
-	beta_offset[p]    ~ dnorm(0,1)
-	beta[p] <- group_beta_MEAN + beta_offset[p] * group_beta_PRECISION
-	#beta[p] ~ dnorm(group_beta_MEAN, group_beta_PRECISION)
+	beta[p] ~ dnorm(group_beta_MEAN, group_beta_PRECISION) T(0,1)
 }
 
 # MODEL-SPECIFIC: CALCULATION OF PRESENT SUBJECTIVE VALUES

diff --git a/ddToolbox/models/parametric_models/beta_delta_models/mixedBetaDelta.jags b/ddToolbox/models/parametric_models/beta_delta_models/mixedBetaDelta.jags
@@ -8,14 +8,17 @@ model{
 
 # estimates of mean/sd of beta and delta were taken from Franck, C. T., Koffarnus, M. N., House, L. L., & Bickel, W. K. (2014). Accurate characterization of delay discounting: A multiple model approach using approximate bayesian model selection and a unified discounting measure. Journal of the Experimental Analysis of Behavior, 103(1), 218–233.
 
+# delta is slope
 delta_MEAN      <- 0.9995
 delta_PRECISION <- 1/(0.0009^2)
+
+# beta is intercept
 beta_MEAN <- 0.84
 beta_PRECISION <- 1/(0.11^2)
 
 for (p in 1:nRealExperimentFiles){
 	delta[p]    ~ dnorm(delta_MEAN, delta_PRECISION)
-    beta[p]     ~ dnorm(beta_MEAN, beta_PRECISION) T(0,)
+    beta[p]     ~ dnorm(beta_MEAN, beta_PRECISION) T(0,1)
 }
 
 

diff --git a/ddToolbox/models/parametric_models/beta_delta_models/separateBetaDelta.jags b/ddToolbox/models/parametric_models/beta_delta_models/separateBetaDelta.jags
@@ -8,14 +8,17 @@ model{
 
 # estimates of mean/sd of beta and delta were taken from Franck, C. T., Koffarnus, M. N., House, L. L., & Bickel, W. K. (2014). Accurate characterization of delay discounting: A multiple model approach using approximate bayesian model selection and a unified discounting measure. Journal of the Experimental Analysis of Behavior, 103(1), 218–233.
 
+# delta is slope
 delta_MEAN      <- 0.9995
 delta_PRECISION <- 1/(0.0009^2)
+
+# beta is intercept
 beta_MEAN <- 0.84
 beta_PRECISION <- 1/(0.11^2)
 
 for (p in 1:nRealExperimentFiles){
 	delta[p]    ~ dnorm(delta_MEAN, delta_PRECISION)
-    beta[p]     ~ dnorm(beta_MEAN, beta_PRECISION) T(0,)
+    beta[p]     ~ dnorm(beta_MEAN, beta_PRECISION) T(0,1)
 }
 
 # MODEL-SPECIFIC: CALCULATION OF PRESENT SUBJECTIVE VALUES
@@ -26,8 +29,8 @@ for (t in 1:length(ID)) {
 
 # RESPONSE ERROR PARAMETERS ====================================================
 
-epsilon_alpha <- 1.1
-epsilon_beta <- 10.9
+epsilon_alpha <- 1+0.1
+epsilon_beta <- 1+9.9
 
 for (p in 1:nRealExperimentFiles){
     epsilon[p]  ~ dbeta(epsilon_alpha , epsilon_beta ) T(,0.5)