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#' Print sim.seqtest
#'
#' This function prints the \code{sim.seqtest.cor} object
#'
#' @param x \code{sim.seqtest.cor} object.
#' @param ... further arguments passed to or from other methods.
#'
#' @author
#' Takuya Yanagida \email{takuya.yanagida@@univie.ac.at}
#'
#' @seealso
#' \code{\link{sim.seqtest.cor}}, \code{\link{plot.sim.seqtest.cor}}
#'
#' @references
#' Schneider, B., Rasch, D., Kubinger, K. D., & Yanagida, T. (2015).
#' A Sequential triangular test of a correlation coefficient's null-hypothesis: 0 \eqn{< \rho \le \rho}0.
#' \emph{Statistical Papers, 56}, 689-699.
#'
#' @method print sim.seqtest.cor
#'
#' @export
#'
#' @examples
#' \dontrun{
#'
#' #---------------------------------------------
#' # Determine optimal k and nominal type-II-risk
#' # H0: rho <= 0.3, H1: rho > 0.3
#' # alpha = 0.01, beta = 0.05, delta = 0.25
#'
#' # Step 1: Determine the optimal size of subsamples (k)
#'
#' sim.obj <- sim.seqtest.cor(rho.sim = 0.3, k = seq(4, 16, by = 1), rho = 0.3,
#' alternative = "greater",
#' delta = 0.25, alpha = 0.05, beta = 0.05,
#' runs = 10000, output = FALSE)
#'
#' print(sim.obj)
#'
#' # Step 2: Determine the optimal nominal type-II-risk based on
#' # the optimal size of subsamples (k) from step 1
#'
#' sim.obj <- sim.seqtest.cor(rho.sim = 0.55, k = 16, rho = 0.3,
#' alternative = "greater",
#' delta = 0.25, alpha = 0.05, beta = seq(0.05, 0.15, by = 0.01),
#' runs = 10000, output = FALSE)
#'
#' print(sim.obj)
#' }
print.sim.seqtest.cor <- function(x, ...) {
#-----------------------------------------------------------------------------------
# Input Check
if (!inherits(x, "sim.seqtest.cor")) {
stop("Object is not a sim.seqtest.cor object!")
}
#-----------------------------------------------------------------------------------
# Main function
cat("\n Statistical Simulation for the Sequential Triangular Test\n\n")
if (x$spec$alternative == "two.sided") {
cat(" H0: rho =", x$spec$rho, " versus H1: rho !=", x$spec$rho, "\n\n")
} else {
if (x$spec$alternative == "less") {
cat(" H0: rho >=", x$spec$rho, " versus H1: rho <", x$spec$rho, "\n\n")
} else {
cat(" H0: rho <=", x$spec$rho, " versus H1: rho >", x$spec$rho, "\n\n")
}
}
# length(k) == 1 & length(beta) == 1
if (length(x$spec$k) == 1 & length(x$spec$beta) == 1) {
cat(" Nominal type-I-risk (alpha): ", x$spec$alpha, "\n",
" Nominal type-II-risk (beta): ", x$spec$beta, "\n",
" Practical relevant effect (delta):", x$spec$delta, "\n",
" n in each sub-sample (k): ", x$spec$k, "\n\n",
" Simulated data based on rho: ", x$spec$rho.sim, "\n",
" Simulation runs: ", x$spec$runs, "\n")
if (x$spec$rho.sim == x$spec$rho) {
cat("\n Estimated empirical type-I-risk (alpha):", x$res$alpha.emp, "\n",
" Average number of steps (AVN): ", x$res$AVN, "\n",
" Average number of sample pairs (ASN): ", x$res$ASN, "\n\n")
} else {
cat("\n Estimated empirical type-II-risk (beta):", formatC(x$res$beta.emp, digits = x$spec$digits, format = "f"), "\n",
" Average number of steps (AVN): ", formatC(x$res$AVN, digits = x$spec$digits, format = "f"), "\n",
" Average number of sample pairs (ASN): ", formatC(x$res$ASN, digits = x$spec$digits, format = "f"), "\n\n")
}
# length(k) != 1 | length(beta) != 1
} else {
# length(k) > 1
if (length(x$spec$k) > 1) {
cat(" Nominal type-I-risk (alpha): ", x$spec$alpha, "\n",
" Nominal type-II-risk (beta): ", x$spec$beta, "\n",
" Practical relevant effect (delta):", x$spec$delta, "\n\n",
" Simulated data based on rho: ", x$spec$rho.sim, "\n",
" Simulation runs: ", x$spec$runs, "\n\n")
cat(" Estimated empirical type-I-risk (alpha):\n")
###
for (i in x$spec$k) {
cat(paste0(" k = ", i, ": ", formatC(x$res$p.H1[x$res$k == i], digits = x$spec$digits, format = "f")), "\n")
}
cat("\n Average number of steps (AVN):\n")
for (i in x$spec$k) {
cat(paste0(" k = ", i, ": ", formatC(x$res$AVN[x$res$k == i], digits = x$spec$digits, format = "f")), "\n")
}
cat("\n Average number of sample pairs (ASN):\n")
for (i in x$spec$k) {
cat(paste0(" k = ", i, ": ", formatC(x$res$ASN[x$res$k == i], digits = x$spec$digits, format = "f")), "\n")
}
# length(beta) > 1
} else {
cat(" Nominal type-I-risk (alpha): ", x$spec$alpha, "\n",
" Practical relevant effect (delta):", x$spec$delta, "\n",
" n in each sub-sample (k): ", x$spec$k, "\n\n",
" Simulated data based on rho: ", x$spec$rho.sim, "\n",
" Simulation runs: ", x$spec$runs, "\n\n")
cat(" Estimated empirical type-II-risk (beta):\n")
###
digits <- max(nchar(x$spec$beta)) - 2
for (i in x$spec$beta) {
cat(paste0(" Nominal beta = ", formatC(i, format = "f", digits = digits), ": ",
formatC(x$res$beta.emp[x$res$beta.nom == i], digits = x$spec$digits, format = "f")), "\n")
}
cat("\n Average number of steps (AVN):\n")
for (i in x$spec$beta) {
cat(paste0(" Nominal beta = ", formatC(i, format = "f", digits = digits), ": ",
formatC(x$res$AVN[x$res$beta.nom == i], digits = x$spec$digits, format = "f")), "\n")
}
cat("\n Average number of sample pairs (ASN):\n")
for (i in x$spec$beta) {
cat(paste0(" Nominal beta = ", formatC(i, format = "f", digits = digits), ": ",
formatC(x$res$ASN[x$res$beta.nom == i], digits = x$spec$digits, format = "f")), "\n")
}
}
cat("\n")
}
}
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