roll (version 1.0.0)

roll_lm: Rolling Linear Models

Description

A parallel function for computing rolling linear models of time-series data.

Usage

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

Arguments

x

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

y

matrix or xts object. Rows are observations and column is the dependent variable.

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:
• coefficients
  An object of the same class and dimension as x with the rolling coefficients.
• r.squared
  An object of the same class as x with the rolling r-squareds.

See Also

setThreadOptions for thread options via RcppParallel.

Examples

n_vars <- 10
n_obs <- 1000
x <- matrix(rnorm(n_obs * n_vars), nrow = n_obs, ncol = n_vars)
y <- matrix(rnorm(n_obs), nrow = n_obs, ncol = 1)

# 252-day rolling regression
result <- roll_lm(x, y, 252)

# Equivalent to 'na.rm = TRUE'
result <- roll_lm(x, y, 252, min_obs = 1)

# Expanding window
result <- roll_lm(x, y, n_obs, min_obs = 1)

# Exponential decay
weights <- 0.9 ^ (251:0)
result <- roll_lm(x, y, 252, weights, min_obs = 1)