roll (version 1.1.2)

roll_sd: Rolling Standard Deviations

Description

A function for computing rolling standard deviations of time-series data.

Usage

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

Arguments

x

matrix or xts object. 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.

Value

An object of the same class and dimension as x with the rolling standard deviations.

Details

The denominator used gives an unbiased estimate of the standard deviation, so if the weights are the default then the divisor n - 1 is obtained.

Examples

# NOT RUN {
n_vars <- 3
n_obs <- 15
x <- matrix(rnorm(n_obs * n_vars), nrow = n_obs, ncol = n_vars)

# rolling standard deviations
result <- roll_sd(x, 5)

# rolling standard deviations with exponential decay
weights <- 0.9 ^ (5:1)
result <- roll_sd(x, 5, weights)
# }