fit_rolling_var: Estimate rolling-window ordinary VAR networks

In idiographic: Person-Specific (Idiographic) and Heterogeneous Complex Networks

Estimate rolling-window ordinary VAR networks

Description

Fits fit_var() over ordered, overlapping windows within each subject. This is a simple time-varying idiographic baseline: every window uses the same lag construction, scaling, within-person centring, and tidy coefficient access as fit_var(), but returns one coefficient table per window.

Usage

fit_rolling_var(
  data,
  vars,
  id = NULL,
  day = NULL,
  beep = NULL,
  window_size,
  step = 1L,
  scale = TRUE,
  center_within = TRUE,
  delete_missings = TRUE,
  min_obs = NULL,
  subject = NULL,
  keep_fits = FALSE
)

Arguments

data	A data.frame or matrix with columns for variables and optional id/day/beep columns.
vars	Character vector of variable names.
id	Character. Name of the person-ID column, or NULL for a single series.
day	Character. Name of the day/session column, or NULL.
beep	Character. Name of the measurement-occasion column, or NULL.
window_size	Integer number of ordered rows per rolling window.
step	Integer number of rows to advance between windows. Default 1.
scale	Logical. Whether to standardize variables inside each window. Default TRUE.
center_within	Logical. Whether to centre within person inside each window when more than one id is present. Default TRUE.
delete_missings	Logical. Drop incomplete current/lagged rows. Default TRUE.
min_obs	Integer or NULL. Keep only subjects with at least this many observations before rolling.
subject	Optional vector naming the subject(s) to analyse.
keep_fits	Logical. Store successful var_result fits? Default FALSE.

Value

A rolling_var_result with ⁠$estimates⁠, ⁠$windows⁠, ⁠$failures⁠, and optionally ⁠$fits⁠. ⁠$estimates⁠ is a tidy coefficient table with subject/window metadata plus network, from, to, and weight.

Examples

set.seed(1)
d <- data.frame(id = 1, day = rep(1:5, each = 20),
                beep = rep(1:20, 5),
                A = rnorm(100), B = rnorm(100), C = rnorm(100))
tv <- fit_rolling_var(d, vars = c("A", "B", "C"), id = "id",
                  day = "day", beep = "beep",
                  window_size = 40, step = 20, scale = FALSE)
head(tv$estimates)

idiographic documentation built on Aug. 4, 2026, 1:07 a.m.