R/estimation_methods.R

In multiRL: Reinforcement Learning Tools for Multi-Armed Bandit

#' @title
#' Estimate Methods
#' @name estimation_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
#'    \code{estimate = "???"}.
#'
#' @param estimate
#'  Estimate method that you want to use,
#'    see \link[multiRL]{estimate}
#' @param data
#'  A data frame in which each row represents a single trial,
#'    see \link[multiRL]{data}
#' @param colnames
#'  Column names in the data frame,
#'    see \link[multiRL]{colnames}
#' @param behrule
#'  The agent's implicitly formed internal rule,
#'    see \link[multiRL]{behrule}
#' @param ids
#'  The Subject ID of the participant whose data needs to be fitted.
#' @param models
#'  Reinforcement Learning Models
#' @param funcs
#'  The functions forming the reinforcement learning model,
#'    see \link[multiRL]{funcs}
#' @param priors
#'  Prior probability density function of the free parameters,
#'    see \link[multiRL]{priors}
#' @param settings
#'  Other model settings,
#'    see \link[multiRL]{settings}
#' @param lowers
#'  Lower bound of free parameters in each model.
#' @param uppers
#'  Upper bound of free parameters in each model.
#' @param control
#'  Settings manage various aspects of the iterative process,
#'    see \link[multiRL]{control}
#' @param ...
#'  Additional arguments passed to internal functions.
#'
#' @returns An S3 object of class \code{DataFrame} containing, for each model,
#'  the estimated optimal parameters and associated model fit metrics.
#'
estimation_methods <- function(
  estimate,

  data,
  colnames,
  behrule,
  ids = NULL,

  models,
  funcs = NULL,
  priors = NULL,
  settings = NULL,

  lowers,
  uppers,
  control,
  ...
) {
  result <- switch(
    EXPR = estimate,
    "MLE" = {
      estimate_1_MLE(
        data = data,
        behrule = behrule,
        ids = ids,
        colnames = colnames,

        models = models,
        funcs = funcs,
        priors = priors,
        settings = settings,

        lowers = lowers,
        uppers = uppers,
        control = control
      )
    },
    "MAP" = {
      estimate_1_MAP(
        data = data,
        colnames = colnames,
        behrule = behrule,
        ids = ids,

        models = models,
        funcs = funcs,
        priors = priors,
        settings = settings,

        lowers = lowers,
        uppers = uppers,
        control = control
      )
    },
    "ABC" = {
      estimate_2_ABC(
        data = data,
        colnames = colnames,
        behrule = behrule,
        ids = ids,

        models = models,
        funcs = funcs,
        priors = priors,
        settings = settings,

        lowers = lowers,
        uppers = uppers,
        control = control
      )
    },
    "RNN" = {
      estimate_2_RNN(
        data = data,
        colnames = colnames,
        behrule = behrule,
        ids = ids,

        models = models,
        funcs = funcs,
        priors = priors,
        settings = settings,

        lowers = lowers,
        uppers = uppers,
        control = control
      )
    },
  )

  return(result)
}