estimation_methods: Estimate Methods

Description

This function provides a unified interface for four estimation methods: Maximum Likelihood Estimation (MLE), Maximum A Posteriori (MAP), Approximate Bayesian Computation (ABC), and Recurrent Neural Network (RNN), allowing users to execute different methods simply by setting estimate = "???".

Usage

estimation_methods(
  estimate,
  data,
  colnames,
  behrule,
  ids = NULL,
  models,
  funcs = NULL,
  priors = NULL,
  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

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.
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.