Compare idiographic estimators on one dataset — compare

Fits one or more idiographic estimators to the same data and returns a tidy per-method/per-network comparison table. This is a reporting layer: it does not define a new model, and each row is computed from the estimator's own summary() method plus common edge-table accessors.

Usage

compare_idiographic(
  data,
  vars,
  estimators = c("var", "graphical_var"),
  id = NULL,
  day = NULL,
  beep = NULL,
  estimator_args = list(),
  keep_fits = FALSE
)

Arguments

data: A data.frame or matrix with columns for variables and optional id/day/beep columns.
vars: Character vector of variable names.
estimators: Character vector naming registered network estimators to fit. Built-in values are "var", "var_bayes", "graphical_var", "mlvar", "mlvar_bayes", "mlvar_mplus", "usem", and "gimme".
id: Character. Name of the person-ID column, or NULL.
day: Character. Name of the day/session column, or NULL.
beep: Character. Name of the measurement-occasion column, or NULL.
estimator_args: Named list of per-estimator argument lists, e.g. list(graphical_var = list(n_lambda = 8), usem = list(temporal = "ar")).
keep_fits: Logical. Store fitted model objects? Default FALSE.

Value

A model_comparison object with $comparison, $failures, and optionally $fits. $comparison is a tidy data.frame with one row per method/network.

Examples

set.seed(1)
d <- data.frame(id = 1, day = rep(1:4, each = 15),
                beep = rep(1:15, 4),
                A = rnorm(60), B = rnorm(60), C = rnorm(60))
cmp <- compare_idiographic(
  d, vars = c("A", "B", "C"), id = "id", day = "day", beep = "beep",
  estimators = c("var", "graphical_var"),
  estimator_args = list(graphical_var = list(n_lambda = 5))
)
cmp$comparison
#>          method         network n_nodes n_edges density mean_abs_weight
#> 1           var        temporal       3       6       1      0.09014205
#> 2           var contemporaneous       3       3       1      0.08260114
#> 3 graphical_var        temporal       3       0       0      0.00000000
#> 4 graphical_var contemporaneous       3       0       0      0.00000000
#>   n_positive n_negative n_self max_abs_weight
#> 1          3          3      3      0.1899219
#> 2          2          1      0      0.1304348
#> 3          0          0      0      0.0000000
#> 4          0          0      0      0.0000000