rcv_d: 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,
  algorithm,
  lowers,
  uppers,
  control,
  ...
)

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.

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
algorithm: Algorithm packages that multiRL supports, see algorithm
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.

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")),
   algorithm = "NLOPT_GN_MLSL",
   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, sample = 100, iter = 100)
 )