loop_psychonetrics: Parallel loop for psychonetrics

Description

A convenience wrapper for running repeated psychonetrics analyses in parallel. Replaces the typical boilerplate of makeCluster / clusterExport / parLapply / stopCluster with a single function call. Particularly useful for bootstrapping, simulation studies, and cross-validation.

Variables referenced in the expression that exist in the calling environment are automatically exported to parallel workers, so explicit clusterExport calls are not needed in most cases.

Usage

loop_psychonetrics(expr, reps = 1000, nCores = 1,
                   export = character(0),
                   packages = c("psychonetrics", "dplyr"),
                   verbose = TRUE, envir = parent.frame())

Value

A list of length reps, where each element is the result of evaluating expr once.

Arguments

expr: An expression (code block) to evaluate on each iteration. Typically enclosed in curly braces {...}. The expression should return a single object (e.g., a psychonetrics model).
reps: Number of replications (default: 1000).
nCores: Number of CPU cores to use. Set to 1 for sequential execution (default), or higher for parallel execution via makePSOCKcluster.
export: Character vector of additional variable names to export to parallel workers. Usually not needed because variables referenced in expr are auto-detected and exported.
packages: Character vector of packages to load on parallel workers (default: c("psychonetrics", "dplyr")).
verbose: Logical; if TRUE (default), shows a progress bar via pbapply.
envir: The environment in which to look up variables for auto-export and to evaluate the expression in sequential mode. Defaults to the calling environment.

Details

When nCores > 1, a PSOCK cluster is created and automatically cleaned up when the function exits (even on error). The specified packages are loaded on each worker, and variables referenced in expr are automatically detected and exported.

For bootstrapping, use this function together with bootstrap = "nonparametric" in the model constructor and aggregate_bootstraps to summarize the results.

Examples

Run this code

# \donttest{
library(dplyr)
data(NA2020)

# Fit the original model:
model <- ggm(NA2020, estimator = "FIML") %>% runmodel

# Bootstrap with pruning (parallel, 100 reps):
bootstraps <- loop_psychonetrics({
  ggm(NA2020, bootstrap = "nonparametric",
      estimator = "FIML") %>%
    runmodel %>%
    prune(alpha = 0.05)
}, reps = 100, nCores = 2)

# Aggregate and inspect:
boot_agg <- aggregate_bootstraps(
  sample = model %>% prune(alpha = 0.05),
  bootstraps = bootstraps
)

# View results:
parameters(boot_agg)
CIplot(boot_agg, "omega", split0 = TRUE)
# }