roll_mean: Rolling Means

In roll: Rolling and Expanding Statistics

Rolling Means

Description

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

Usage

roll_mean(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 means.

Examples

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

# rolling means with complete windows
roll_mean(x, width = 5)

# rolling means with partial windows
roll_mean(x, width = 5, min_obs = 1)

# expanding means with partial windows
roll_mean(x, width = n, min_obs = 1)

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

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