R/document_reduction.R

In multiRL: Reinforcement Learning Tools for Multi-Armed Bandit

#' @title Dimension Reduction Methods (ABC)
#' @name reduction
#' @description
#'  Specifies the dimension reduction method for summary statistics in
#'  Approximate Bayesian Computation (ABC). High-dimensional summary
#'  statistics can lead to the "curse of dimensionality," where the
#'  algorithm struggles to find a solution. Reducing dimensions helps retain
#'  the "fingerprint" of the original data while removing noise, allowing the
#'  program to efficiently identify the underlying parameters.
#'
#' @section Methods:
#' \itemize{
#'    \item \code{NULL}:
#'        No compression is applied. This is suitable for smaller datasets
#'          where the number of features (e.g., blocks * responses) is low
#'          (typically < 200). The \code{ncomp} parameter is ignored.
#'
#'    \item \code{"PLS"} (Partial Least Squares):
#'        A supervised method that compresses summary statistics into a
#'          lower-dimensional space defined by \code{ncomp}. It finds linear
#'          combinations of statistics that maximize covariance with the
#'          parameters, "guiding" the compression to prioritize information
#'          most relevant to parameter estimation.
#'
#'    \item \code{"PCA"} (Principal Component Analysis):
#'        An unsupervised method that compresses information into a
#'          lower-dimensional space defined by \code{ncomp}. It identifies
#'          orthogonal directions (principal components) that capture the
#'          maximum variance within the summary statistics themselves,
#'          preserving the data's most characteristic features without
#'          considering the parameters.
#' }
#'
#' @section Related Parameters:
#' \itemize{
#'    \item \code{ncomp [int]}
#'        The number of components to retain after compression. By default,
#'          this is the number of blocks in the experiment. An excessive
#'          number of blocks or actions can create a high-dimensional summary
#'          space, making it hard for ABC to converge. Specifying an
#'          appropriate \code{ncomp} is crucial when using "PLS" or "PCA".
#' }
#'
#' @section Example:
#' \preformatted{ # supported reduction methods
#'  control = list(
#'    reduction = c(NULL, "PCA", "PLS"),
#'    ncomp = NULL
#'  )
#' }
#'
NULL