Move code for CPT

R/modeling_cpt.R

+# parameters
+parameters <- c("alpha",
+                "gamma",
+                "delta",
+                "rho")
+n_chains <- 4
+
+
+# prepare data for JAGS
+
+## select strategy-parameter combination
+
+choices_MCMC <- choices %>%
+  filter(strategy == "piecewise" & boundary == "relative" & s == 1-.9, 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$i)
+)
+
+
+# MCMC sampling
+samples <- jags.parallel(data,
+                         parameters,
+                         model.file = "JAGS/cpt_trial_level.txt",
+                         inits = NULL,
+                         n.chains = n_chains,
+                         n.iter = 10000,
+                         n.burnin = 5000,
+                         n.thin = 1,
+                         n.cluster = n_chains,
+                         jags.seed = 888)
+
+
+# MCMC diagnostics
+samples$BUGSoutput$summary
+mcmcplots::mcmcplot(samples)
+

pilot-study.Rmd

 ---
-title: "Sampling in DfE: Pilot Study"
+title: "Sampling in DfE: Simulation Study"
 author: "Linus Hof, Veronika Zilker & Thorsten Pachur"
 bibliography: sampling-strategies-in-dfe.bib
 csl: apa.csl
@@ -39,7 +39,7 @@ A formal introduction to sampling in DfE and the data generating models of this
 
 # Method
 
-## Data Set
+## Test set
 
 Under each condition, i.e., strategy-parameter combinations, all gambles are played by 100 synthetic agents. We test a set of gambles, in which one of the prospects contains a safe outcome and the other two risky outcomes (*safe-risky gambles*). Therefore, 60 gambles from an initial set of 10,000 are sampled. Both, outcomes and probabilities are drawn from uniform distributions, ranging from 0 to 20 for outcomes and from .01 to .99 for probabilities of the lower risky outcomes $p_{low}$. The probabilities of the higher risky outcomes are $1-p_{low}$, respectively. To omit dominant prospects, safe outcomes fall between both risky outcomes. The table below contains the test set of 60 gambles. Sampling of gambles was stratified, randomly drawing an equal number of 20 gambles with no, an attractive, and an unattractive rare outcome. Risky outcomes are considered *"rare"* if their probability is $p < .2$ and *"attractive"* (*"unattractive"*) if they are higher (lower) than the safe outcome.
 
@@ -581,67 +581,5 @@ 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