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Build an ordinary least-squares VAR network

Source: R/var.R
fit_var.Rd

Fits a transparent VAR(1) baseline from intensive longitudinal data using ordinary least squares: current variables are regressed on an intercept and lag-1 predictors. The lag construction, scaling, within-person centring, and day-boundary behaviour match fit_graphical_var(), but no regularization or EBIC model selection is applied.

Usage

fit_var(
  data,
  vars,
  id = NULL,
  day = NULL,
  beep = NULL,
  lags = 1L,
  scale = TRUE,
  center_within = TRUE,
  delete_missings = TRUE,
  min_obs = NULL,
  subject = NULL
)

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.

lags

Integer. Only 1 is supported.

scale

Logical. Whether to standardize variables before lagging. Default TRUE.

center_within

Logical. Whether to centre within person 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.

subject

Optional vector naming the subject(s) to analyse.

Value

A var_result object with temporal OLS coefficients, residual covariance, residual precision, contemporaneous partial correlations, and tidy access through edges(), coefs(), nodes(), and summary().

Examples

set.seed(1)
d <- data.frame(id = 1, A = rnorm(80), B = rnorm(80), C = rnorm(80))
fit <- fit_var(d, vars = c("A", "B", "C"), id = "id")
edges(fit)
#>           network from to       weight
#> 1        temporal    C  B  0.113592795
#> 2        temporal    B  C -0.056084686
#> 3        temporal    B  A  0.042216979
#> 4        temporal    A  B -0.030796224
#> 5        temporal    A  C -0.024626586
#> 6        temporal    C  A  0.004625333
#> 7 contemporaneous    A  B -0.302794840
#> 8 contemporaneous    A  C  0.175739891
#> 9 contemporaneous    B  C  0.042015104