roll (version 1.1.2)

roll_lm: Rolling Linear Models

Description

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

Usage

roll_lm(x, y, width, weights = rep(1, width), intercept = TRUE,
  min_obs = width, complete_obs = TRUE, na_restore = FALSE,
  online = TRUE)

Arguments

x

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

y

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

width

integer. Window size.

weights

vector. Weights for each observation within a window.

intercept

logical. Either TRUE to include or FALSE to remove the intercept.

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 list containing the following components:

coefficients

A list of objects with the rolling coefficients for each y. An object is the same class and dimension (with an added column for the intercept) as x.

r.squared

A list of objects with the rolling r-squareds for each y. An object is the same class as x.

std.error

A list of objects with the rolling standard errors for each y. An object is the same class and dimension (with an added column for the intercept) as x.

Examples

# NOT RUN {
n_vars <- 3
n_obs <- 15
x <- matrix(rnorm(n_obs * n_vars), nrow = n_obs, ncol = n_vars)
y <- matrix(rnorm(n_obs), nrow = n_obs, ncol = 1)

# rolling regressions
result <- roll_lm(x, y, 5)

# rolling regressions with exponential decay
weights <- 0.9 ^ (5:1)
result <- roll_lm(x, y, 5, weights)
# }