Example Code for Bayesian estimation of CPT parameters

pilot-study.Rmd

@@ -581,5 +581,67 @@ choices %>%
 
 ### Modeling Choices in Cumulative Prospect Theory 
 
+```{r}
+# parameters
+parameters <- c("alpha",
+                "gamma",
+                "delta",
+                "rho")
+n_chains <- 4
+```
+
+```{r}
+# prepare data for JAGS 
+
+## select strategy-parameter combination
+
+choices_MCMC <- choices %>% 
+  filter(strategy == "piecewise" & boundary == "relative" & s == 1, a == 7) %>% 
+  mutate(choice_A = case_when(choice == "A" ~ 1,
+                              choice == "B" ~ 0),
+         i = row_number(),
+         a_p2 = 1-a_p1, 
+         b_o1 = b,
+         b_p1 = b_p,
+         b_o2 = 0,
+         b_p2 = 0) %>% 
+  select(strategy:rare, a_p1, a_o1, a_p2, a_o2, a_ev, b_p1, b_o1, b_p2, b_o2, choice_A, i)
+
+data = list(
+      resp = choices_MCMC$choice_A, 
+      x_A = choices_MCMC$a_o2, # higher risky outcome 
+      y_A = choices_MCMC$a_o1, # lower risky outcome
+      x_B = choices_MCMC$b_o1, # safe outcome
+      y_B = choices_MCMC$b_o2, # 0
+      px_A = choices_MCMC$a_p2, # probability higher risky outcome
+      py_A = choices_MCMC$a_p1, # probability lower risky outcome
+      px_B = choices_MCMC$b_p1, # probability safe outcome (1)
+      py_B = choices_MCMC$b_p2, # 0
+      min_i = min(choices_MCMC$i),
+      max_i = max(choices_MCMC$i)
+    )
+```
+
+```{r}
+# MCMC sampling
+samples <- jags.parallel(data,
+                         parameters,
+                         model.file = "JAGS/cpt_trial_level.txt",
+                         inits = NULL, 
+                         n.chains = n_chains, 
+                         n.iter = 10000,
+                         n.burnin = 5000, 
+                         n.thin = 1,
+                         n.cluster = n_chains, 
+                         jags.seed = 888)
+
+
+# MCMC diagnostics
+samples$BUGSoutput$summary
+mcmcplots::mcmcplot(samples)
+```
+
+
+
 
 # References