estimate_1_MLE: Estimation Method: Maximum Likelihood Estimation (MLE)

In multiRL: Reinforcement Learning Tools for Multi-Armed Bandit

Estimation Method: Maximum Likelihood Estimation (MLE)

Description

This function essentially applies estimate_1_LBI() to each subject's data, estimating subject-specific optimal parameters based on maximum likelihood. Because the fitting process for each subject is independent, the procedure can be accelerated using parallel computation.

Usage

estimate_1_MLE(
  data,
  colnames,
  behrule,
  ids = NULL,
  models,
  funcs = NULL,
  priors,
  settings = NULL,
  lowers,
  uppers,
  control,
  ...
)

Arguments

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
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 DataFrame containing, for each model, the estimated optimal parameters and associated model fit metrics.

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