R/fitted.R

In aalfons/robustHD: Robust Methods for High-Dimensional Data

# --------------------------------------
# Author: Andreas Alfons
#         Erasmus Universiteit Rotterdam
# --------------------------------------

#' Extract fitted values from a sequence of regression models
#'
#' Extract fitted values from a sequence of regression models, such as submodels
#' along a robust or groupwise least angle regression sequence, or sparse least
#' trimmed squares regression models for a grid of values for the penalty
#' parameter.
#'
#' @method fitted seqModel
#' @aliases fitted.rlars fitted.grplars fitted.tslarsP
#'
#' @param object  the model fit from which to extract fitted values.
#' @param p  an integer giving the lag length for which to extract fitted
#' values (the default is to use the optimal lag length).
#' @param s  for the \code{"seqModel"} method, an integer vector giving the
#' steps of the submodels for which to extract the fitted values (the default
#' is to use the optimal submodel).  For the \code{"sparseLTS"} method, an
#' integer vector giving the indices of the models for which to extract fitted
#' values.  If \code{fit} is \code{"both"}, this can be a list with two
#' components, with the first component giving the indices of the reweighted
#' fits and the second the indices of the raw fits.  The default is to use the
#' optimal model for each of the requested estimators.  Note that the optimal
#' models may not correspond to the same value of the penalty parameter for the
#' reweighted and the raw estimator.
#' @param fit  a character string specifying which fitted values to extract.
#' Possible values are \code{"reweighted"} (the default) for the fitted values
#' from the reweighted estimator, \code{"raw"} for the fitted values from the
#' raw estimator, or \code{"both"} for the fitted values from both estimators.
#' @param drop  a logical indicating whether to reduce the dimension to a
#' vector in case of only one step.
#' @param \dots  for the \code{"tslars"} method, additional arguments to be
#' passed down to the \code{"seqModel"} method.  For the other methods,
#' additional arguments are currently ignored.
#'
#' @return
#' A numeric vector or matrix containing the requested fitted values.
#'
#' @author Andreas Alfons
#'
#' @seealso \code{\link[stats]{fitted}}, \code{\link{rlars}},
#' \code{\link{grplars}}, \code{\link{rgrplars}}, \code{\link{tslarsP}},
#' \code{\link{rtslarsP}}, \code{\link{tslars}}, \code{\link{rtslars}},
#' \code{\link{sparseLTS}}
#'
#' @example inst/doc/examples/example-fitted.R
#'
#' @keywords regression
#'
#' @import stats
#' @export

fitted.seqModel <- function(object, s = NA, drop = !is.null(s), ...) {
  getComponent(object, "fitted.values", s=s, drop=drop, ...)
}


#' @rdname fitted.seqModel
#' @method fitted tslars
#' @export

fitted.tslars <- function(object, p, ...) {
  ## initializations
  # check lag length
  if(missing(p) || !is.numeric(p) || length(p) == 0) {
    p <- object$pOpt
  } else p <- p[1]
  pMax <- object$pMax
  if(p < 1) {
    p <- 1
    warning("lag length too small, using lag length 1")
  } else if(p > pMax) {
    p <- pMax
    warning(sprintf("lag length too large, using maximum lag length %d", p))
  }
  ## extract fitted values for specified lag length
  fitted(object$pFit[[p]], ...)
}


#' @rdname fitted.seqModel
#' @method fitted perrySeqModel
#' @export
fitted.perrySeqModel <- function(object, ...) {
  finalModel <- object$finalModel
  if(is.null(finalModel)) stop("final model not available")
  fitted(finalModel, ...)
}


#' @rdname fitted.seqModel
#' @method fitted sparseLTS
#' @export

fitted.sparseLTS <- function(object, s = NA,
                             fit = c("reweighted", "raw", "both"),
                             drop = !is.null(s), ...) {
  getComponent(object, "fitted.values", s=s, fit=fit, drop=drop, ...)
}