fit_p: Step 3: Optimizing parameters to fit real data

In multiRL: Reinforcement Learning Tools for Multi-Armed Bandit

Step 3: Optimizing parameters to fit real data

Description

Step 3: Optimizing parameters to fit real data

Usage

fit_p(
  estimate,
  data,
  colnames,
  behrule,
  ids = NULL,
  funcs = NULL,
  priors = NULL,
  settings = NULL,
  models,
  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
ids	The Subject ID of the participant whose data needs to be fitted.
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
models	Reinforcement Learning Models
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.fitting. A List containing, for each model, the estimated optimal parameters and associated model fit metrics.

Example

 # fitting
 fitting.MLE <- multiRL::fit_p(
   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")
   ),

   models = list(multiRL::TD, multiRL::RSTD, multiRL::Utility),
   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)
 )

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