roll_quantile: Rolling Quantiles

In roll: Rolling and Expanding Statistics

Rolling Quantiles

Description

A function for computing the rolling and expanding quantiles of time-series data.

Usage

roll_quantile(x, width, weights = rep(1, width), p = 0.5,
  min_obs = width, complete_obs = FALSE, na_restore = FALSE,
  online = FALSE)

Arguments

x	vector or matrix. Rows are observations and columns are variables.
width	integer. Window size.
weights	vector. Weights for each observation within a window.
p	numeric. Probability between zero and one.
min_obs	integer. Minimum number of observations required to have a value within a window, otherwise result is NA.
complete_obs	logical. If TRUE then rows containing any missing values are removed, if FALSE then each value is used.
na_restore	logical. Should missing values be restored?
online	logical. Process observations using an online algorithm.

Details

The methodology for computing the quantiles is based on the inverse of the empirical distribution function with averaging at discontinuities (see "Definition 2" in Hyndman and Fan, 1996).

Value

An object of the same class and dimension as x with the rolling and expanding quantiles.

References

Hyndman, R.J. and Fan, Y. (1996). "Sample quantiles in statistical packages." American Statistician, 50(4), 361-365.

Examples

n <- 15
x <- rnorm(n)
weights <- 0.9 ^ (n:1)

# rolling quantiles with complete windows
roll_quantile(x, width = 5)

# rolling quantiles with partial windows
roll_quantile(x, width = 5, min_obs = 1)

# expanding quantiles with partial windows
roll_quantile(x, width = n, min_obs = 1)

# expanding quantiles with partial windows and weights
roll_quantile(x, width = n, min_obs = 1, weights = weights)

roll documentation built on May 29, 2024, 6:02 a.m.