| hdquantile {Hmisc} | R Documentation |
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.
hdquantile(x, probs = seq(0, 1, 0.25),
se = FALSE, na.rm = FALSE, names = TRUE, weights=FALSE)
x |
a numeric vector |
probs |
vector of quantiles to compute |
se |
set to TRUE to also compute standard errors |
na.rm |
set to TRUE to remove NAs from x
before computing quantiles |
names |
set to FALSE to prevent names attributions from
being added to quantiles and standard errors |
weights |
set to TRUE to return a "weights"
attribution with the matrix of weights used in the H-D estimator
corresponding to order statistics, with columns corresponding to
quantiles. |
A Fortran routine is used to compute the jackknife leave-out-one
quantile estimates. Standard errors are not computed for quantiles 0 or
1 (NAs are returned).
A vector of quantiles. If se=TRUE this vector will have an
attribute se added to it, containing the standard errors. If
weights=TRUE, also has a "weights" attribute which is a matrix.
Frank Harrell
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.
set.seed(1) x <- runif(100) hdquantile(x, (1:3)/4, se=TRUE) ## Not run: # 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) ## End(Not run)