rcv_d: Step 2: Generating fake data for parameter and model recovery

In multiRL: Reinforcement Learning Tools for Multi-Armed Bandit

Step 2: Generating fake data for parameter and model recovery

Description

Step 2: Generating fake data for parameter and model recovery

Usage

rcv_d(
  estimate,
  data,
  colnames,
  behrule,
  id = NULL,
  models,
  funcs = NULL,
  priors = NULL,
  settings = NULL,
  lowers,
  uppers,
  control,
  ...
)

Arguments

estimate	Estimate method that you want to use, see estimate
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
id	The ID of the subject whose experimental structure (e.g., trial order) will be used as the template for generating all simulated data. Defaults to the first subject found in the input data.
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
lowers	Lower bound of free parameters in each model.
uppers	Upper bound of free parameters in each model.
control	Settings manage various aspects of the iterative process, see control
...	Additional arguments passed to internal functions.

Value

An S3 object of class multiRL.recovery.

simulate

A List containing, for each model, the parameters used to simulate the data.

recovery

A List containing, for each model, the parameters estimated as optimal by the algorithm.

Example

 # recovery
 recovery.MLE <- multiRL::rcv_d(
   estimate = "MLE",

   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")
   ),
   id = 1,

   models = list(multiRL::TD, multiRL::RSTD, multiRL::Utility),
   priors = list(
     list(
       alpha = function(x) {stats::rbeta(n = 1, shape1 = 2, shape2 = 2)},
       beta = function(x) {stats::rexp(n = 1, rate = 1)}
     ),
     list(
       alphaN = function(x) {stats::rbeta(n = 1, shape1 = 2, shape2 = 2)},
       alphaP = function(x) {stats::rbeta(n = 1, shape1 = 2, shape2 = 2)},
       beta = function(x) {stats::rexp(n = 1, rate = 1)}
     ),
     list(
       alpha = function(x) {stats::rbeta(n = 1, shape1 = 2, shape2 = 2)},
       beta = function(x) {stats::rexp(n = 1, rate = 1)},
       gamma = function(x) {stats::rbeta(n = 1, shape1 = 2, shape2 = 2)}
     )
   ),
   settings = list(name = c("TD", "RSTD", "Utility")),

   lowers = list(c(0, 0), c(0, 0, 0), c(0, 0, 0)),
   uppers = list(c(1, 5), c(1, 1, 5), c(1, 5, 1)),
   control = list(core = 10, iter = 100, sample = 100)
 )

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