gevrPbGen <- function(n, R, theta, information) {
data1 <- rgevr(n, R, theta[1], theta[2], theta[3])
y1 <- tryCatch(gevrFit(data1, method = "mle"), error = function(w) {return(NULL)}, warning = function(w) {return(NULL)})
if(is.null(y1)) NA
else gevrTestStat(y1, information)
}
#' GEVr Parametric Bootstrap Score Test
#'
#' Parametric bootstrap score test procedure to assess goodness-of-fit to the GEVr distribution.
#' @param data Data should be contain n rows, each a GEVr observation.
#' @param bootnum Number of bootstrap replicates.
#' @param information To use expected (default) or observed information in the test.
#' @param allowParallel Should the bootstrap procedure be run in parallel or not. Defaults to false.
#' @param numCores If allowParallel is true, specify the number of cores to use.
#' @examples
#' ## Not run
#' ## Generate some data from GEVr
#' # x <- rgevr(200, 5, loc = 0.5, scale = 1, shape = 0.25)
#' # gevrPbScore(x, bootnum = 99)
#' @return
#' \item{statistic}{Test statistic.}
#' \item{p.value}{P-value for the test.}
#' \item{theta}{Initial value of theta used in the test.}
#' \item{effective_bootnum}{Effective number of bootstrap replicates (only those that converged are used).}
#' @details GEVr data (in matrix x) should be of the form \eqn{x[i,1] > x[i, 2] > \cdots > x[i, r]} for each observation \eqn{i = 1, \ldots, n}.
#' @import parallel
#' @references Bader B., Yan J., & Zhang X. (2015). Automated Selection of r for the r Largest Order Statistics Approach with Adjustment for Sequential Testing. Department of Statistics, University of Connecticut.
#' @export
gevrPbScore <- function(data, bootnum, information = c("expected", "observed"), allowParallel = FALSE, numCores = 1) {
data <- as.matrix(data)
n <- nrow(data)
R <- ncol(data)
information <- match.arg(information)
y <- tryCatch(gevrFit(data, method = "mle"), error = function(w) {return(NULL)}, warning = function(w) {return(NULL)})
if(is.null(y))
stop("Maximum likelihood failed to converge at initial step")
theta <- y$par.ests
stat <- gevrTestStat(y, information)
if(allowParallel == TRUE) {
cl <- makeCluster(numCores)
fun <- function(cl) {
parSapply(cl, 1:bootnum, function(i,...) {gevrPbGen(n, R, theta, information)})
}
teststat <- fun(cl)
stopCluster(cl)
} else {
teststat <- replicate(bootnum, gevrPbGen(n, R, theta, information))
}
teststat <- teststat[!is.na(teststat)]
eff <- length(teststat)
p <- (sum(teststat > stat) + 1) / (eff + 2)
out <- list(stat, p, theta, eff)
names(out) <- c("statistic", "p.value", "theta", "effective_bootnum")
out
}
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