roll (version 1.0.5)

roll_eigen: Rolling Eigenvalues and Eigenvectors

Description

A parallel function for computing rolling eigenvalues and eigenvectors of time-series data.

Usage

roll_eigen(data, width, weights = rep(1, width), center = TRUE, scale = FALSE, min_obs = width, complete_obs = TRUE, na_restore = FALSE, parallel_for = c("rows", "cols"))

Arguments

data

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?

parallel_for

character. Executes a "for" loop in which iterations run in parallel by rows or cols.

Value

A list containing the following components: A list containing the following components:

See Also

setThreadOptions for thread options via RcppParallel.

Examples

n_vars <- 10
n_obs <- 1000
data <- matrix(rnorm(n_obs * n_vars), nrow = n_obs, ncol = n_vars)

# 252-day rolling eigenvalues and eigenvectors
result <- roll_eigen(data, 252)

# Equivalent to 'na.rm = TRUE'
result <- roll_eigen(data, 252, min_obs = 1)

# Expanding window
result <- roll_eigen(data, n_obs, min_obs = 1)

# Exponential decay
weights <- 0.9 ^ (251:0)
result <- roll_eigen(data, 252, weights, min_obs = 1)