roll (version 1.1.2)

roll_cor: Rolling Correlation Matrices

Description

A function for computing rolling correlation matrices 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

matrix or xts object. Rows are observations and columns are variables.

y

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.

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.

Value

A cube with each slice the rolling correlation matrix.

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.

Examples

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

# rolling correlation matrices
result <- roll_cor(x, width = 5)

# rolling correlation matrices with exponential decay
weights <- 0.9 ^ (5:1)
result <- roll_cor(x, width = 5, weights = weights)
# }