rpl_e: Step 4: Replaying the experiment with optimal parameters

In multiRL: Reinforcement Learning Tools for Multi-Armed Bandit

Step 4: Replaying the experiment with optimal parameters

Description

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.

Value

An S3 object of class multiRL.replay. A List containing, for each subject and each fitted model, the estimated optimal parameters, along with the resulting multiRL.model and multiRL.summary objects obtained by replaying the model with those parameters.

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")
 )

multiRL documentation built on March 31, 2026, 5:06 p.m.