GIMME: Group Iterative Multiple Model Estimation

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%) of individuals.
Individual-level paths: Additional edges specific to each person, found after group paths are established.

Estimation is delegated to idiographic::fit_gimme(), the clean-room home of the temporal idiographic estimators, whose search reproduces the upstream gimme package (>= 10.0) exactly (verified at tolerance 0 on path counts and per-person coefficient matrices in idiographic's own parity suite). 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); accepted for API compatibility and not forwarded to idiographic::fit_gimme(), which does not implement subgrouping either. Default 0.50.
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

The object returned by idiographic::fit_gimme(): an S3 object of class c("net_gimme", "cograph_network", "list"). It is a superset of the pre-0.9.0 in-package field contract – every element below is present, alongside idiographic's own additions (contemp_cov, contemp_cov_avg, contemp_is_cov). Elements:

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).
temporal_avg, contemporaneous_avg: p x p group-average coefficient matrices.
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.

The object additionally carries idiographic's netobject fields (weights, nodes, edges, directed, data, meta, node_groups) so it renders directly with cograph. print(), summary() and plot() dispatch to idiographic's methods, not to Nestimate's: in particular summary() returns a tidy data.frame rather than printing.

Results changed in 0.9.0

Before 0.9.0 the search ran in an in-package implementation that was not upstream-gimme-exact. Delegating to idiographic::fit_gimme() changed which paths the search selects on the same data – individual-level paths in particular – so numeric results are not comparable with Nestimate <= 0.8.5. The returned object also gained fields (see Value); nothing was removed.

Examples