R/PLNPCA.R

In PLN-team/PLNmodels: Poisson Lognormal Models

#' Poisson lognormal model towards Principal Component Analysis
#'
#' Fit the PCA variants of the Poisson lognormal with a variational algorithm. Use the (g)lm syntax for model specification (covariates, offsets).
#'
#' @param formula an object of class "formula": a symbolic description of the model to be fitted.
#' @param data an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called.
#' @param subset an optional vector specifying a subset of observations to be used in the fitting process.
#' @param weights an optional vector of observation weights to be used in the fitting process.
#' @param ranks a vector of integer containing the successive ranks (or number of axes to be considered)
#' @param control a list-like structure for controlling the optimization, with default generated by [PLNPCA_param()].  See the associated documentation.
#' for details.
#'
#' @return an R6 object with class [`PLNPCAfamily`], which contains
#' a collection of models with class [`PLNPCAfit`]
#'
#' @rdname PLNPCA
#' @examples
#' #' ## Use future to dispatch the computations on 2 workers
#' \dontrun{
#' future::plan("multisession", workers = 2)
#' }
#'
#' data(trichoptera)
#' trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
#' myPCA <- PLNPCA(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, ranks = 1:5)
#'
#' # Shut down parallel workers
#' \dontrun{
#' future::plan("sequential")
#' }
#' @seealso The classes [`PLNPCAfamily`] and [`PLNPCAfit`], and the configuration function [PLNPCA_param()].
#' @importFrom stats model.frame model.matrix model.response model.offset
#' @export
PLNPCA <- function(formula, data, subset, weights, ranks = 1:5, control = PLNPCA_param()) {

  ## Temporary test for deprecated use of list()
  if (!inherits(control, "PLNmodels_param"))
    stop("We now use the function PLNPCA_param() to generate the list of parameters that controls the fit:
    replace 'list(my_arg = xx)' by PLN_param(my_arg = xx) and see the documentation of PLNPCA_param().")

  ## extract the data matrices and weights
  args <- extract_model(match.call(expand.dots = FALSE), parent.frame())

  ## Instantiate the collection of PLN models, initialized by PLN with full rank
  if (control$trace > 0) cat("\n Initialization...")
  myPCA <- PLNPCAfamily$new(ranks, args$Y, args$X, args$O, args$w, args$formula, control)

  ## Adjust the PLN models
  if (control$trace > 0) cat("\n\n Adjusting", length(ranks), "PLN models for PCA analysis.\n")
  myPCA$optimize(control$config_optim)

  ## Post-treatments: pseudo-R2, rearrange criteria and prepare PCA visualization
  if (control$trace > 0) cat("\n Post-treatments")
  myPCA$postTreatment(control$config_post, control$config_optim)

  if (control$trace > 0) cat("\n DONE!\n")
  myPCA
}

#' Control of PLNPCA fit
#'
#' Helper to define list of parameters to control the PLNPCA fit. All arguments have defaults.
#'
#' @param backend optimization back used, either "nlopt" or "torch". Default is "nlopt"
#' @param trace a integer for verbosity.
#' @param config_optim a list for controlling the optimizer (either "nlopt" or "torch" backend). See details
#' @param config_post a list for controlling the post-treatments (optional bootstrap, jackknife, R2, etc.). See details
#' @param inception Set up the parameters initialization: by default, the model is initialized with a multivariate linear model applied on
#'    log-transformed data, and with the same formula as the one provided by the user. However, the user can provide a PLNfit (typically obtained from a previous fit),
#'    which sometimes speeds up the inference.
#'
#' @return list of parameters configuring the fit.
#'
#' @inherit PLN_param details
#' @export
PLNPCA_param <- function(
    backend       = "nlopt",
    trace         = 1      ,
    config_optim  = list() ,
    config_post   = list() ,
    inception     = NULL     # pretrained PLNfit used as initialization
) {

  if (!is.null(inception)) stopifnot(isPLNfit(inception))

  ## post-treatment config
  config_pst <- config_post_default_PLNPCA
  config_pst[names(config_post)] <- config_post
  config_pst$trace <- trace

  ## optimization config
  backend <- match.arg(backend)
  stopifnot(backend %in% c("nlopt", "torch"))
  if (backend == "nlopt") {
    stopifnot(config_optim$algorithm %in% available_algorithms_nlopt)
    config_opt <- config_default_nlopt
  }
  if (backend == "torch") {
    stopifnot(config_optim$algorithm %in% available_algorithms_torch)
    config_opt <- config_default_torch
  }
  config_opt[names(config_optim)] <- config_optim
  config_opt$trace <- trace

  structure(list(
    backend       = backend   ,
    trace         = trace     ,
    config_optim  = config_opt,
    config_post   = config_pst,
    inception     = inception   ), class = "PLNmodels_param")
}