Build a user-specified unified SEM network

Fits person-specific unified Structural Equation Models (uSEM) for intensive longitudinal data. A uSEM combines lagged directed effects, optional contemporaneous directed effects, and optional residual covariances in one SEM. Unlike fit_gimme(), this function does no automated path search: the model is fixed by temporal, contemporaneous, residual_cov, and paths. With trim = TRUE, idiographic uses an independent clean-room modification-index entry and z-value pruning layer over the declared candidate set.

Usage

fit_usem(
  data,
  vars,
  id,
  time = NULL,
  day = NULL,
  beep = NULL,
  min_obs = NULL,
  subject = NULL,
  temporal = c("ar", "all", "none"),
  contemporaneous = c("none", "all"),
  residual_cov = TRUE,
  trim = FALSE,
  trim_alpha = 0.05,
  trim_fit_criteria = 3L,
  cfi_cutoff = 0.95,
  tli_cutoff = 0.95,
  rmsea_cutoff = 0.08,
  srmr_cutoff = 0.08,
  paths = NULL,
  exogenous = NULL,
  standardize = FALSE,
  estimator = "ml",
  seed = NULL
)

Arguments

data: A data.frame in long format.
vars: Character vector of time-varying variables.
id: Character string naming the person-ID column.
time: Character string naming the within-person ordering column, or NULL.
day: Character string naming the day/session column, or NULL. When supplied, lag pairs are formed only within the same (id, day) block.
beep: Character string naming the measurement-occasion column, or NULL. Used with day to order observations when time is not supplied.
min_obs: Integer or NULL. Keep only subjects with at least this many observations.
subject: Optional vector naming the subject(s) to analyse.
temporal: "ar" (own-lag only; default), "all" (all lagged predictors), "none", or a character vector of lavaan regressions such as "A ~ Blag".
contemporaneous: "none" (default), "all" (all directed lag-0 predictors except self-regressions), or lavaan regressions such as "B ~ A".
residual_cov: Logical. Estimate residual covariances among current endogenous variables? Default TRUE.
trim: Logical. If TRUE, treat temporal, contemporaneous, and residual_cov as an eligible search space: start from the structural baseline, add paths by modification index until fit criteria are met, then prune weak paths. This is an idiographic clean-room search layer, not a clone of any external package. Default FALSE fits the exact fixed syntax.
trim_alpha: Significance level used for modification-index entry and z-value pruning when trim = TRUE. Default 0.05.
trim_fit_criteria: Number of fit criteria that must pass before forward search stops. Default 3.
cfi_cutoff, tli_cutoff, rmsea_cutoff, srmr_cutoff: Fit thresholds used by trimmed uSEM.
paths: Extra lavaan syntax lines to include unchanged.
exogenous: Optional subset of vars to treat as exogenous current variables. They can predict endogenous variables but are not outcomes.
standardize: Logical. Standardize variables per person before fitting.
estimator: Lavaan estimator. Default "ml".
seed: Optional random seed.

Value

A net_usem object with average $temporal, $contemporaneous, and $residual_cov matrices, per-subject matrices in $subjects, a tidy coefficient table from coefs(), fit indices, syntax, labels, and configuration metadata.

Examples

# \donttest{
set.seed(1)
d <- data.frame(
  id = rep(1:4, each = 30),
  t = rep(seq_len(30), 4),
  A = rnorm(120), B = rnorm(120), C = rnorm(120)
)
fit <- fit_usem(d, vars = c("A", "B", "C"), id = "id", time = "t")
edges(fit)
#>        network from to      weight
#> 1 residual_cov    B  C -0.12338761
#> 2 residual_cov    A  C  0.09763793
#> 3 residual_cov    A  B  0.04235390
# }

Usage

Arguments

Value

See also

Examples