Skip to contents

Monte Carlo Recovery of a Known Structure with CFA-Boosting

Source: R/simulate_cfa_recovery.R
simulate_cfa_recovery.Rd

The confirmatory counterpart of simulate_recovery. Data are simulated from a known population, a simple structure model over every item is handed to cfa_boosting, and the question is how often the pipeline removes the items that deserve removal and only those.

Usage

simulate_cfa_recovery(
  n = 500,
  loading = 0.65,
  items_per_factor = 5,
  n_factors = 3,
  phi = 0.3,
  n_cross = 1,
  cross_loading = 0.4,
  n_low = 1,
  low_loading = 0.2,
  n_categories = 5,
  skew = c("symmetric", "skewed"),
  n_reps = 100,
  seed = NULL,
  verbose = TRUE,
  n_cores = 1,
  timeout = 120,
  ...
)

# S3 method for class 'simulate_cfa_recovery'
print(x, ...)

Arguments

n

Sample size(s). Vector values are crossed into conditions. Default 500.

loading

Population loading(s) of the good items. Default 0.65.

items_per_factor

Number of good items per factor. Default 5.

n_factors

Number of factors in the population model. Default 3.

phi

Interfactor correlation. Default 0.30.

n_cross

Number of cross-loading items added to the pool (they load cross_loading on factors 1 and 2). Default 1.

cross_loading

Loading of the cross-loading items on both factors. Default 0.40.

n_low

Number of weak items added to the pool. Default 1.

low_loading

Loading of the weak items. Default 0.20.

n_categories

Number of ordered response categories. Default 5.

skew

Category threshold shape: "symmetric" (default) or "skewed".

n_reps

Replications per condition. Default 100.

seed

Optional integer seed for reproducibility; NULL (default) leaves the RNG untouched.

verbose

Print progress per condition. Default TRUE.

n_cores

Number of worker processes used to fit the replications of a condition. Default 1 (sequential). A crossed design is hundreds of efa_boosting() runs, so this is usually the difference between minutes and hours; parallel::detectCores() - 1 is a practical choice. The datasets are always generated sequentially from seed, so results do not depend on the number of cores.

timeout

Seconds allowed per replication. Default 120; NULL removes the cap. A single pathological dataset can otherwise grind for many minutes and stall the condition it belongs to. A capped replication is recorded with stop_reason = "timeout". Needs the R.utils package.

...

Ignored.

x

A simulate_cfa_recovery object.

Value

An object of class simulate_cfa_recovery: a list with summary (one row per condition), replications, conditions, population, model (the syntax handed to the pipeline) and call.

Details

The question is not the same one the exploratory simulation asks, and the difference is not cosmetic. In simulate_recovery the structure is discovered, so recovery means every retained item landed on its true factor. Here the structure is imposed by the researcher, so the assignment cannot be wrong by construction and what is at stake is the item selection: the specified model deliberately includes the contaminated items, and a pipeline that works removes them without taking good items with them.

Two further quantities are worth watching. covs_added counts the residual covariances the pipeline introduced: the population has none, so every one of them is a false positive, which is the modification index objection of MacCallum, Roznowski and Necowitz (1992) turned into a number. And exact is deliberately strict, requiring the removed set to equal the contaminated set exactly, because sensitivity and specificity can both look respectable while no single replication got the answer right.

The cross loading item is specified on the first factor and the weak item as well, which is what a researcher would do when writing the model without knowing which items are faulty.

See also

simulate_recovery, cfa_boosting

Examples

# \donttest{
sim <- simulate_cfa_recovery(n = 300, n_reps = 2, seed = 1)
#> Condition 1/1: N=300, lambda=0.65, items/factor=5 (17 items total)
#> 
  [                          ]   0% (0/2)  elapsed 0s  left ~?     
  [=============             ]  50% (1/2)  elapsed 3s  left ~3s     
  [==========================] 100% (2/2)  elapsed 6s  left ~0s     
sim
#> 
#> CFA-Boosting recovery simulation
#> --------------------------------------------------------------------
#> 1 condition(s) x 2 replications
#> Population: 3 factors, roles: cross=1, good=15, low=1
#> 
#>    n loading items_per_factor n_reps convergence_rate exact_rate sensitivity
#>  300    0.65                5      2                1          1           1
#>  specificity mean_retained mean_covs_added mean_rmsea
#>            1            15               0      0.012
#> 
#> exact_rate = the removed set equals the contaminated set, item for item.
#> sensitivity = contaminated items removed; specificity = good items kept.
#> mean_covs_added = residual covariances added; the population has none,
#> so anything above zero is capitalization on chance.
plot(sim, metric = "sensitivity")
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?

# }