## File: hdquantile.Rd

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hmisc 4.2-0-1
 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061 \name{hdquantile} \alias{hdquantile} \title{Harrell-Davis Distribution-Free Quantile Estimator} \description{ Computes the Harrell-Davis (1982) quantile estimator and jacknife standard errors of quantiles. The quantile estimator is a weighted linear combination or order statistics in which the order statistics used in traditional nonparametric quantile estimators are given the greatest weight. In small samples the H-D estimator is more efficient than traditional ones, and the two methods are asymptotically equivalent. The H-D estimator is the limit of a bootstrap average as the number of bootstrap resamples becomes infinitely large. } \usage{ hdquantile(x, probs = seq(0, 1, 0.25), se = FALSE, na.rm = FALSE, names = TRUE, weights=FALSE) } \arguments{ \item{x}{a numeric vector} \item{probs}{vector of quantiles to compute} \item{se}{set to \code{TRUE} to also compute standard errors} \item{na.rm}{set to \code{TRUE} to remove \code{NA}s from \code{x} before computing quantiles} \item{names}{set to \code{FALSE} to prevent names attributions from being added to quantiles and standard errors} \item{weights}{set to \code{TRUE} to return a \code{"weights"} attribution with the matrix of weights used in the H-D estimator corresponding to order statistics, with columns corresponding to quantiles.} } \details{ A Fortran routine is used to compute the jackknife leave-out-one quantile estimates. Standard errors are not computed for quantiles 0 or 1 (\code{NA}s are returned). } \value{ A vector of quantiles. If \code{se=TRUE} this vector will have an attribute \code{se} added to it, containing the standard errors. If \code{weights=TRUE}, also has a \code{"weights"} attribute which is a matrix. } \references{ Harrell FE, Davis CE (1982): A new distribution-free quantile estimator. Biometrika 69:635-640. Hutson AD, Ernst MD (2000): The exact bootstrap mean and variance of an L-estimator. J Roy Statist Soc B 62:89-94. } \author{Frank Harrell} \seealso{\code{\link{quantile}}} \examples{ set.seed(1) x <- runif(100) hdquantile(x, (1:3)/4, se=TRUE) \dontrun{ # Compare jackknife standard errors with those from the bootstrap library(boot) boot(x, function(x,i) hdquantile(x[i], probs=(1:3)/4), R=400) } } \keyword{univar}