R/ael.mean.R

In markean/bayesEL: Bayesian Empicial Likelihood

#' Adjusted empirical likelihood for mean
#'
#' Adjusted empirical likelihood for mean
#'
#' @param theta a vector of parameters to be tested.
#' @param x a matrix or vector of data. Each row is an observation vector.
#' @param abstol an optional value for the absolute convergence tolerance. Defaults to 1e-8.
#' @param maxit an optional value for the maximum number of iterations. Defaults to 25.
#'
#' @return An object with S3 class "ael"
#'
#' @examples
#' theta <- 10
#' x <- rnorm(100)
#' ael.mean(theta, x)
#'
#' @import Matrix
#' @export
ael.mean <- function(theta, x, abstol = 1e-8, maxit = 25) {
  ## Data
  x <- as.matrix(x)
  if (length(theta) != ncol(x))
    stop("length(theta) != ncol(x)")
  if (rankMatrix(x) != ncol(x))
    stop("model matrix must have full column rank")
  colnames(x) <- NULL
  n <- nrow(x)
  # new data(for AEL)
  an <- log(n) / 2
  x <- rbind(x, theta - an * (colMeans(x) - theta))

  ## Result
  el.result <- el.mean(theta, x, abstol, maxit)
  result <- list()
  class(result) <- "ael"
  result$logLR <- el.result$logLR
  result$logL <- el.result$logL
  result$w <- el.result$w
  result$lambda <- el.result$lambda
  result$iterations <- el.result$iterations
  result
}