roll_min: Rolling Minimums

In roll: Rolling and Expanding Statistics

Description

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

Usage

roll_min(x, width, weights = rep(1, width), min_obs = width,
  complete_obs = FALSE, na_restore = FALSE, online = TRUE)

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.
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.

Value

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

Examples

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

# rolling minimums with complete windows
roll_min(x, width = 5)

# rolling minimums with partial windows
roll_min(x, width = 5, min_obs = 1)

# expanding minimums with partial windows
roll_min(x, width = n, min_obs = 1)

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

Example output

 [1]         NA         NA         NA         NA -1.2463055 -0.6214704
 [7] -0.8302279 -0.8302279 -0.8302279 -0.8302279 -0.8302279 -0.5901291
[13] -1.2108329 -1.2108329 -1.2108329
 [1] -1.2463055 -1.2463055 -1.2463055 -1.2463055 -1.2463055 -0.6214704
 [7] -0.8302279 -0.8302279 -0.8302279 -0.8302279 -0.8302279 -0.5901291
[13] -1.2108329 -1.2108329 -1.2108329
 [1] -1.246306 -1.246306 -1.246306 -1.246306 -1.246306 -1.246306 -1.246306
 [8] -1.246306 -1.246306 -1.246306 -1.246306 -1.246306 -1.246306 -1.246306
[15] -1.246306
 [1] -1.246306 -1.246306 -1.246306 -1.246306 -1.246306 -1.246306 -1.246306
 [8] -1.246306 -1.246306 -1.246306 -1.246306 -1.246306 -1.246306 -1.246306
[15] -1.246306

roll documentation built on July 13, 2020, 5:09 p.m.