estimate_1_MLE: 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,
  algorithm,
  lowers,
  uppers,
  control,
  ...
)

Value

An S3 object of class DataFrame containing, for each model, the estimated optimal parameters and associated model fit metrics.

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
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.