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#' bootstrap critical value of statistic
#'
#' @param samplesize number of observations in the sample
#' @param statistic test statistic to be used
#' @param tuning tuning parameter used for the test statistic (\code{NULL} stands for no tuning parameter needed)
#' @param k_estimator value of the estimated \code{shape} parameter
#' @param boot.param number of bootstrap iterations
#' @param alpha significance level of the test
#'
#' @return returns the critical value for the goodness-of-fit test using the \code{statistic}.
#'
#' @examples
#' crit.values(samplesize=20,statistic=HME1,tuning=1,k_estimator=2,boot.param=100,alpha=0.05)
#'
#'@export
crit.values <- function(samplesize,statistic,tuning=NULL,k_estimator,boot.param=500,alpha=0.05)
{
n=samplesize
y <- rep(0,boot.param)
if(!is.null(tuning))
{
for (j in 1:boot.param)
{
BS <- stats::rgamma(n,k_estimator,1)
BS_k_estimator <- gamma_est(BS)[1]
BS_lambda_estimator <- mean(BS)/BS_k_estimator
x_BS=BS/BS_lambda_estimator
y[j]=statistic(x_BS,BS_k_estimator,tuning)
}
} else {
for (j in 1:boot.param)
{
BS <- stats::rgamma(n,k_estimator,1)
BS_k_estimator <- gamma_est(BS)[1]
BS_lambda_estimator <- mean(BS)/BS_k_estimator
x_BS=BS/BS_lambda_estimator
y[j]=statistic(x_BS,BS_k_estimator)
}
}
Tn <- sort(y)
k=floor((1-alpha)*boot.param)
p_n <-Tn[k]+(1-alpha)*(Tn[k+1]-Tn[k])
return(p_n)
}
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