build_gimme: GIMME: Group Iterative Multiple Model Estimation
In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

build_gimme

R Documentation

GIMME: Group Iterative Multiple Model Estimation

Description

Estimates person-specific directed networks from intensive longitudinal data using the unified Structural Equation Modeling (uSEM) framework. Implements a data-driven search that identifies:

Group-level paths: Directed edges present for a majority (default 75\
Individual-level paths: Additional edges specific to each person, found after group paths are established.

Uses lavaan for SEM estimation and modification indices. Accepts a single data frame with an ID column (not CSV directories).

Usage

build_gimme(
  data,
  vars,
  id,
  time = NULL,
  ar = TRUE,
  standardize = FALSE,
  groupcutoff = 0.75,
  subcutoff = 0.5,
  paths = NULL,
  exogenous = NULL,
  hybrid = FALSE,
  rmsea_cutoff = 0.05,
  srmr_cutoff = 0.05,
  nnfi_cutoff = 0.95,
  cfi_cutoff = 0.95,
  n_excellent = 2L,
  seed = NULL
)

Arguments

`data`	A `data.frame` in long format with columns for person ID, time-varying variables, and optionally a time/beep column.
`vars`	Character vector of variable names to model.
`id`	Character string naming the person-ID column.
`time`	Character string naming the time/order column, or `NULL`. When provided, data is sorted by `id` then `time` before lagging.
`ar`	Logical. If `TRUE` (default), autoregressive paths (each variable predicting itself at lag 1) are included as fixed paths.
`standardize`	Logical. If `TRUE` (default `FALSE`), variables are standardized per person before estimation.
`groupcutoff`	Numeric between 0 and 1. Proportion of individuals for whom a path must be significant to be added at group level. Default `0.75`.
`subcutoff`	Numeric. Not used (reserved for future subgrouping).
`paths`	Character vector of lavaan-syntax paths to force into the model (e.g., `"V2~V1lag"`). Default `NULL`.
`exogenous`	Character vector of variable names to treat as exogenous. Default `NULL`.
`hybrid`	Logical. If `TRUE`, also searches residual covariances. Default `FALSE`.
`rmsea_cutoff`	Numeric. RMSEA threshold for excellent fit (default 0.05).
`srmr_cutoff`	Numeric. SRMR threshold for excellent fit (default 0.05).
`nnfi_cutoff`	Numeric. NNFI/TLI threshold for excellent fit (default 0.95).
`cfi_cutoff`	Numeric. CFI threshold for excellent fit (default 0.95).
`n_excellent`	Integer. Number of fit indices that must be excellent to stop individual search. Default `2`.
`seed`	Integer or `NULL`. Random seed for reproducibility.

Value

An S3 object of class "net_gimme" containing:

temporal: p x p matrix of group-level temporal (lagged) path counts – entry [i,j] = number of individuals with path j(t-1)->i(t).
contemporaneous: p x p matrix of group-level contemporaneous path counts – entry [i,j] = number of individuals with path j(t)->i(t).
coefs: List of per-person p x 2p coefficient matrices (rows = endogenous, cols = [lagged, contemporaneous]).
psi: List of per-person residual covariance matrices.
fit: Data frame of per-person fit indices (chisq, df, pvalue, rmsea, srmr, nnfi, cfi, bic, aic, logl, status).
path_counts: p x 2p matrix: how many individuals have each path.
paths: List of per-person character vectors of lavaan path syntax.
group_paths: Character vector of group-level paths found.
individual_paths: List of per-person character vectors of individual-level paths (beyond group).
syntax: List of per-person full lavaan syntax strings.
labels: Character vector of variable names.
n_subjects: Integer. Number of individuals.
n_obs: Integer vector. Time points per individual.
config: List of configuration parameters.

Examples


if (requireNamespace("gimme", quietly = TRUE)) {
  # Create simple panel data (3 subjects, 4 variables, 50 time points)
  set.seed(42)
  n_sub <- 3; n_t <- 50; vars <- paste0("V", 1:4)
  rows <- lapply(seq_len(n_sub), function(i) {
    d <- as.data.frame(matrix(rnorm(n_t * 4), ncol = 4))
    names(d) <- vars; d$id <- i; d
  })
  panel <- do.call(rbind, rows)
  res <- build_gimme(panel, vars = vars, id = "id")
  print(res)
}

Nestimate documentation built on April 20, 2026, 5:06 p.m.