Implement unnoisy sampling

R/simulate_piecewise.R

-# load packages
-
 pacman::p_load(tidyverse)
+source("R/fun_cumulative_stats.R") # call functions for computing cumulative stats
 
 # gambles and parameters
 
-gambles <- read_csv("./R/data/gambles/sr_subset.csv")
+gambles <- read_csv("data/gambles/sr_subset.csv")
 n_agents <- 100
 theta <- expand_grid(s = seq(-.5, .4, .1), # probability increment added to unbiased sampling probability of p = .5
-                     sigma = .5, # noise
                      boundary = c("absolute", "relative"), # boundary type
-                     a = c(1, 3, 5, 7)) # boundaries piecewise
-
+                     a = c(1, 3, 5, 7)) # boundaries (number of required wins)
 
 # simulation
 
-source("./R/functions/fun_cumulative.R") # call functions for computing cumulative sums and means
-
 set.seed(8739)
 param_list <- vector("list", length(nrow(theta)))
 for (set in seq_len(nrow(theta))) {  # loop over parameter combinations
@@ -24,7 +19,7 @@ for (set in seq_len(nrow(theta))) {  # loop over parameter combinations
     agents_list <- vector("list", n_agents)
     for (agent in seq_along(1:n_agents)){ # loop over agents
 
-      ## initial values of an agent's sampling process
+      ## initial values of an agent's sampling process (unique trial)
 
       fd <- tibble() # state of ignorance
       p <- .5  # no attention bias
@@ -49,8 +44,7 @@ for (set in seq_len(nrow(theta))) {  # loop over parameter combinations
             single_smpl <- gambles[gamble, ] %>%
               mutate(round = round,
                      attended = attend,
-                     A = sample(x = c(a_o1, a_o2), size = 1, prob = c(a_p1, 1-a_p1)) +
-                       round(rnorm(1, mean = 0, sd = theta[[set, "sigma"]]), 2), # add gaussian noise
+                     A = sample(x = c(a_o1, a_o2), size = 1, prob = c(a_p1, 1-a_p1)),
                      B = NA)
             s <- theta[[set, "s"]]
           } else {
@@ -58,8 +52,7 @@ for (set in seq_len(nrow(theta))) {  # loop over parameter combinations
               mutate(round = round,
                      attended = attend,
                      A = NA,
-                     B = b +
-                       round(rnorm(1, mean = 0, sd = theta[[set, "sigma"]]), 2))
+                     B = b_o1)
             s <- -1*theta[[set, "s"]]
           }
           smpl_round <- bind_rows(smpl_round, single_smpl)
@@ -74,8 +67,7 @@ for (set in seq_len(nrow(theta))) {  # loop over parameter combinations
             single_smpl <- gambles[gamble, ] %>%
               mutate(round = round,
                      attended = attend,
-                     A = sample(x = c(a_o1, a_o2), size = 1, prob = c(a_p1, 1-a_p1)) +
-                       round(rnorm(1, mean = 0, sd = theta[[set, "sigma"]]), 2),
+                     A = sample(x = c(a_o1, a_o2), size = 1, prob = c(a_p1, 1-a_p1)),
                      B = NA)
             s <- theta[[set, "s"]]
           } else {
@@ -83,8 +75,7 @@ for (set in seq_len(nrow(theta))) {  # loop over parameter combinations
               mutate(round = round,
                      attended = attend,
                      A = NA,
-                     B = b +
-                       round(rnorm(1, mean = 0, sd = theta[[set, "sigma"]]), 2))
+                     B = b_o1)
             s <- -1*theta[[set, "s"]]
           }
           smpl_round <- bind_rows(smpl_round, single_smpl)
@@ -135,5 +126,4 @@ for (set in seq_len(nrow(theta))) {  # loop over parameter combinations
   param_list[[set]] <- expand_grid(theta[set, ], all_gambles)
 }
 sim_piecewise <- param_list %>% map_dfr(as.list)
-write_csv(sim_piecewise, "./R/data/simulation/sim_piecewise.csv")
-
+write_csv(sim_piecewise, "data/simulation/sim_piecewise.csv")