R/erfe.R

In erfe: Fits Expectile Regression for Panel Fixed Effect Model

#' Dexpectilize a vector according the a single asymmetric point 
#' @description This function is the main fucntion of the erfe package.
#' It estimates the
#' ERFE model for a panel dataset and for a sequence of asymmetric point
#' \eqn{\tau \in (0, 1)}. When \eqn{\tau=0.5} the function estimate the
#' classical within-transformation estimator and its sandwich covariance
#' matrix.
#' @return Returns a list of list according to the
#' asymmetric points. Each list has objects related
#' to the erfe model such as the asymmetric point,
#' the coefficient-estimate, the standard deviation,
#' the estimated covariance.
#' @author Amadou Barry, \email{barryhafia@@gmail.com}
#' @references Barry, Amadou, Oualkacha, Karim, and Charpentier
#'  Arthur. (2022). \emph{Weighted asymmetric least squares
#'  regression with fixed-effects}.
#'  arXiv preprint arXiv:2108.04737 
#' @param predictors Numeric matrix of covariates/regressors.
#' @param response Numeric vector of response variable.
#' @param asymp Sequence of asymmetric points.
#' @param id Ordered vector of subject ids.
#' @examples
#' set.seed(13)
#' temps_obs <- 5
#' n_subj <- 50
#' sig <- diag(rep(1,temps_obs))
#' id <- rep(1:n_subj, each=temps_obs)
#' rvec <- c(mvtnorm::rmvnorm(n_subj, sigma = sig))
#' fvec <- (1 + rep(rnorm(n_subj) , each=temps_obs))
#' predictors <- cbind(rt(n_subj * temps_obs, df=2, ncp=1.3),
#'  1.2 * fvec + rnorm(n_subj * temps_obs, mean = 0.85, sd = 1.5) )
#' response <- 0.6 * predictors[, 1] + predictors[, 2] + fvec + rvec
#' asymp <- c(0.25,0.5,0.75)
#' erfe(predictors, response, asymp=c(0.25,0.5,0.75), id)
#' @export
#' @importFrom mvtnorm rmvnorm
erfe <- function(predictors, response, asymp = c(0.25, 0.5, 0.75), id) {
  if (!is.matrix(predictors)) {
    warning("predictors has to be a matrix object")
  } else {
    x <- predictors
  }
  if (!is.vector(response)) {
    warning("response has to be a vector object")
  } else {
    y <- response
  }
  
  if (any(is.na(asymp)) || !is.vector(asymp) || any(asymp > 1) || 
      any(asymp < 0)) {
    asymvec <- c(0.25, 0.5, 0.75)
  } else {
    asymvec <- asymp
  }
  lengpan <- unname(unlist(lapply(split(id, id), function(x) length(x))))
  if (length(asymvec) == 1) {
    out_erfe_mod <- erfeVecR(x, y, lengpan, asymvec, id)
    outlist <- c(out_erfe_mod)
  } else {
    out_erfe_mod <- lapply(seq_along(asymvec), function(i) {
      erfeVecR(x, y, lengpan, asymvec[i], id) } ) 
    outlist <- out_erfe_mod
  }
  outlist
}