Nothing
#' 'kernelboot' class object
#'
#' @details
#'
#' Object of class \code{"kernelboot"}, is a list with components including
#'
#' \tabular{ll}{
#' \code{orig.stat} \tab estimates from \code{statistic} on the original data, \cr
#' \code{boot.samples} \tab samples drawn, \cr
#' \code{call} \tab function call, \cr
#' \code{statistic} \tab actual \code{statistic} function that was used, \cr
#' \code{orig.data} \tab original data used for bootstrapping, \cr
#' \code{variables} \tab used variables: it is \code{NULL} for univariate data and
#' for multivariate data it contains two lists of \code{smoothed}
#' and \code{ignored} variables (names or column indexes) during
#' the smoothing phase. \cr
#' \code{type} \tab type of kernel density that was used ("univariate", "product",
#' "multivariate"), \cr
#' \code{param} \tab list of parameters that were used.
#' }
#'
#' \code{param} section contains:
#'
#' \tabular{ll}{
#' \code{R} \tab number of bootstrap iterations, \cr
#' \code{bw} \tab the bandwidth that was used, \cr
#' \code{weights} \tab vector of the weights that were applied, \cr
#' \code{kernel} \tab name of the kernel that was used ("multivariate",
#' "gaussian", "epanechnikov", "rectangular",
#' "triangular", "biweight", "cosine", "optcosine",
#' "none"), \cr
#' \code{shrinked} \tab value of the \code{shrinked} parameter, \cr
#' \code{parallel} \tab indicates if parallel computation was used, \cr
#' \code{random.seed} \tab random seed used to initialize the random number
#' generator (see \code{\link[base]{.Random.seed}}).
#' }
#'
#' @seealso \code{\link{kernelboot}}
#'
#' @name kernelboot-class
NULL
#' Summarize the result of kernelboot
#'
#' @param object \code{kernelboot} class object.
#' @param probs quantiles returned by \code{summary} (see \code{\link{quantile}}).
#' @param \dots further arguments passed to or from other methods.
#' @param na.rm a logical value indicating whether \code{NA} values should be
#' stripped before the computation proceeds.
#'
#' @importFrom stats sd quantile na.omit
#' @export
summary.kernelboot <- function(object, probs = c(0.025, 0.5, 0.975), ..., na.rm = FALSE) {
samp <- object$boot.samples
res <- lapply(1:ncol(samp), function(i) {
x <- samp[, i]
if (is.numeric(x)) {
if (na.rm)
x <- na.omit(x)
c(mean = mean(x), sd = sd(x), quantile(x, probs = probs))
} else {
warning("skipping non-numeric variable")
NA
}
})
names(res) <- colnames(samp)
do.call(rbind, res)
}
#' @export
print.kernelboot <- function(x, ...) {
cat("Call:\n", paste(deparse(x$call), sep = "\n", collapse = "\n"), sep = "")
cat("\n\n")
cat(x$param$R, " samples were generated", sep = "")
if (x$param$kernel == "none") {
cat(" using standard bootstrap.\n", sep = "")
} else {
kernel <- if (x$type == "multivariate") "multivariate gaussian" else x$param$kernel
cat(" from ", x$type, " kernel density with ", kernel, " kernel.\n", sep = "")
}
invisible(x)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.