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#' @title Parameter Estimation Evaluation.
#' @description A function to evaluate the parameter estimation function.
#'
#' @rdname eval.estimation
#' @name eval.estimation
#'
#' @param rdist Random variable generating function.
#' @param edist Parameter estimation function.
#' @param n Sample size.
#' @param rep.num Number of replicates.
#' @param params True parameters of the distribution.
#' @param method Estimation method.
#'
#' @return A list containing the mean and sd of the estimated parameters.\cr
#' \cr
#' na.cont returns the number of "na"s that appeared in the parameter estimation.
#'
#' @author Haizhen Wu and A. Jonathan R. Godfrey.
#'
#' @examples
#' eval.estimation(rdist = rBeta, edist = eBeta, n = 100, rep.num = 50,
#' params = list(shape1 = 1, shape2 = 5))
#' @export eval.estimation
eval.estimation <- function(rdist, edist, n = 20, rep.num = 1e3, params, method = "numerical.MLE"){
k <- length(params)
est.par <- array(NA, dim = c(rep.num,k))
start.time <- proc.time()
for(i in 1:rep.num){
X <- rdist(n=n,params=params)
est.par[i,] <- as.numeric(unlist(edist(X,method=method)))
# print(paste("i=",i))
}
return(list(method = method,
est.mean = apply(est.par,2, mean, na.rm =T),
est.sd = apply(est.par,2, stats::sd, na.rm =T),
time = proc.time() - start.time,
na.cont = sum(is.na(est.par[,1])))
)
}
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