roll_cor: Rolling Correlations

In roll: Rolling and Expanding Statistics

Rolling Correlations

Description

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

Usage

roll_cor(x, y = NULL, width, weights = rep(1, width), center = TRUE,
  scale = TRUE, min_obs = width, complete_obs = TRUE,
  na_restore = FALSE, online = TRUE)

Arguments

x	vector or matrix. Rows are observations and columns are variables.
y	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.
scale	logical. If TRUE then the weighted standard deviation of each variable is used, if FALSE then no scaling is done.
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 pairwise 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 covariance, so if the weights are the default then the divisor n - 1 is obtained.

Value

A cube with each slice the rolling and expanding correlations.

Examples

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

# rolling correlations with complete windows
roll_cor(x, y, width = 5)

# rolling correlations with partial windows
roll_cor(x, y, width = 5, min_obs = 1)

# expanding correlations with partial windows
roll_cor(x, y, width = n, min_obs = 1)

# expanding correlations with partial windows and weights
roll_cor(x, y, width = n, min_obs = 1, weights = weights)

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