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GIMME: Group Iterative Multiple Model Estimation

Source: R/gimme.R
build_gimme.Rd

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

  1. Group-level paths: Directed edges present for a majority (default 75\

  2. 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.

See also

build_network

Examples

# \donttest{
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)
}
#> GIMME Network Analysis
#> ------------------------------ 
#> Subjects:   3 
#> Variables:  4  ( V1, V2, V3, V4 )
#> AR paths:   yes 
#> Hybrid:     no 
#> 
#> Group-level paths found: 0 
#> 
#> Individual-level paths:  mean 0.0, range 0-0
#> 
#> Temporal path counts (lagged):
#>    V1 V2 V3 V4
#> V1  3  0  0  0
#> V2  0  3  0  0
#> V3  0  0  3  0
#> V4  0  0  0  3
#> 
#> Contemporaneous path counts:
#>    V1 V2 V3 V4
#> V1  0  0  0  0
#> V2  0  0  0  0
#> V3  0  0  0  0
#> V4  0  0  0  0
# }