quantile {stats} | R Documentation |

The generic function `quantile`

produces sample quantiles
corresponding to the given probabilities.
The smallest observation corresponds to a probability of 0 and the
largest to a probability of 1.

quantile(x, ...) ## Default S3 method: quantile(x, probs = seq(0, 1, 0.25), na.rm = FALSE, names = TRUE, type = 7, ...)

`x` |
numeric vectors whose sample quantiles are wanted. Missing values are ignored. |

`probs` |
numeric vector of probabilities with values in [0,1]. |

`na.rm` |
logical; if true, any `NA` and `NaN` 's
are removed from `x` before the quantiles are computed. |

`names` |
logical; if true, the result has a `names`
attribute. Set to `FALSE` for speedup with many `probs` . |

`type` |
an integer between 1 and 9 selecting one of the nine quantile algorithms detailed below to be used. |

`...` |
further arguments passed to or from other methods. |

A vector of length `length(probs)`

is returned;
if `names = TRUE`

, it has a `names`

attribute.

`NA`

and `NaN`

values in `probs`

are
propagated to the result.

`quantile`

returns estimates of underlying distribution quantiles
based on one or two order statistics from the supplied elements in
`x`

at probabilities in `probs`

. One of the nine quantile
algorithms discussed in Hyndman and Fan (1996), selected by
`type`

, is employed.

Sample quantiles of type *i* are defined by

*Q[i](p) = (1 - gamma) x[j] + gamma x[j+1],*

where *1 <= i <= 9*,
*(j-m)/n <= p < (j-m+1)/ n*,
*x[j]* is the *j*th order statistic, *n* is the
sample size, and *m* is a constant determined by the sample
quantile type. Here *gamma* depends on the fractional part
of *g = np+m-j*.

For the continuous sample quantile types (4 through 9), the sample
quantiles can be obtained by linear interpolation between the *k*th
order statistic and *p(k)*:

*p(k) = (k - alpha) / (n - alpha - beta + 1),*

where
*α* and *β* are constants determined by
the type. Further, *m = alpha + p(1 - alpha - beta)*, and *gamma = g*.

**Discontinuous sample quantile types 1, 2, and 3**

- Type 1
- Inverse of empirical distribution function.
- Type 2
- Similar to type 1 but with averaging at discontinuities.
- Type 3
- SAS definition: nearest even order statistic.

**Continuous sample quantile types 4 through 9**

- Type 4
*p(k) = k / n*. That is, linear interpolation of the empirical cdf.- Type 5
*p(k) = (k - 0.5) / n*. That is a piecewise linear function where the knots are the values midway through the steps of the empirical cdf. This is popular amongst hydrologists.- Type 6
*p(k) = k / (n + 1)*. Thus*p(k) = E[F(x[k])]*. This is used by Minitab and by SPSS.- Type 7
*p(k) = (k - 1) / (n - 1)*. In this case,*p(k) = mode[F(x[k])]*. This is used by S.- Type 8
*p(k) = (k - 1/3) / (n + 1/3)*. Then*p(k) =~ median[F(x[k])]*. The resulting quantile estimates are approximately median-unbiased regardless of the distribution of`x`

.- Type 9
*p(k) = (k - 3/8) / (n + 1/4)*. The resulting quantile estimates are approximately unbiased for the expected order statistics if`x`

is normally distributed.

Hyndman and Fan (1996) recommend type 8.
The default method is type 7, as used by S and by **R** < 2.0.0.

of the version used in **R** >= 2.0.0, Ivan Frohne and Rob J Hyndman.

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)
*The New S Language*.
Wadsworth & Brooks/Cole.

Hyndman, R. J. and Fan, Y. (1996) Sample quantiles in statistical
packages, *American Statistician*, **50**, 361–365.

`ecdf`

for empirical distributions of which
`quantile`

is the “inverse”;
`boxplot.stats`

and `fivenum`

for computing
“versions” of quartiles, etc.

quantile(x <- rnorm(1001))# Extremes & Quartiles by default quantile(x, probs=c(.1,.5,1,2,5,10,50, NA)/100) ### Compare different types p <- c(0.1,0.5,1,2,5,10,50)/100 res <- matrix(as.numeric(NA), 9, 7) for(type in 1:9) res[type, ] <- y <- quantile(x, p, type=type) dimnames(res) <- list(1:9, names(y)) round(res, 3)

[Package *stats* version 2.5.0 Index]