hdquantile {Hmisc} | R Documentation |

## 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

`x` |
a numeric vector |

`probs` |
vector of quantiles to compute |

`se` |
set to |

`na.rm` |
set to |

`names` |
set to |

`weights` |
set to |

### 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 (`NA`

s are returned).

### Value

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.

### Author(s)

Frank Harrell

### 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.

### See Also

### Examples

```
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)
```

*Hmisc*version 5.1-3 Index]