roll_var: Rolling Variances

In roll: Rolling and Expanding Statistics

Rolling Variances

Description

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

Usage

roll_var(x, width, weights = rep(1, width), center = TRUE,
  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.
center	logical. If TRUE then the weighted mean of each variable is used, if FALSE then zero is used.
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 denominator used gives an unbiased estimate of the variance, so if the weights are the default then the divisor n - 1 is obtained.

Value

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

Examples

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

# rolling variances with complete windows
roll_var(x, width = 5)

# rolling variances with partial windows
roll_var(x, width = 5, min_obs = 1)

# expanding variances with partial windows
roll_var(x, width = n, min_obs = 1)

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

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