Step 4: Replaying the experiment with optimal parameters

Usage

rpl_e(
  result,
  free_params = NULL,
  data,
  colnames,
  behrule,
  ids = NULL,
  models,
  funcs = NULL,
  priors = NULL,
  settings = NULL,
  ...
)

Arguments

result: Result from rcv_d or fit_p
free_params: In order to prevent ambiguity regarding the free parameters, their names can be explicitly defined by the user.
data: A data frame in which each row represents a single trial, see data
colnames: Column names in the data frame, see colnames
behrule: The agent’s implicitly formed internal rule, see behrule
ids: The Subject ID of the participant whose data needs to be fitted.
models: Reinforcement Learning Models
funcs: The functions forming the reinforcement learning model, see funcs
priors: Prior probability density function of the free parameters, see priors
settings: Other model settings, see settings
...: Additional arguments passed to internal functions.

Example

 # info
 data = multiRL::TAB
 colnames = list(
   object = c("L_choice", "R_choice"),
   reward = c("L_reward", "R_reward"),
   action = "Sub_Choose"
 )
 behrule = list(
   cue = c("A", "B", "C", "D"),
   rsp = c("A", "B", "C", "D")
 )

 replay.recovery <- multiRL::rpl_e(
   result = recovery.MLE,

   data = data,
   colnames = colnames,
   behrule = behrule,

   models = list(multiRL::TD, multiRL::RSTD, multiRL::Utility),
   settings = list(name = c("TD", "RSTD", "Utility")),

   omit = c("data", "funcs")
 )

 replay.fitting <- multiRL::rpl_e(
   result = fitting.MLE,

   data = data,
   colnames = colnames,
   behrule = behrule,

   models = list(multiRL::TD, multiRL::RSTD, multiRL::Utility),
   settings = list(name = c("TD", "RSTD", "Utility")),

   omit = c("funcs")
 )