ResIN_boots_prepare: Create a bootstrap plan for re-estimating ResIN objects to...

In ResIN: Conduct Response Item Network (ResIN) Analysis with Social Response Data

Create a bootstrap plan for re-estimating ResIN objects to derive statistical uncertainty estimates

Description

Provides instructions for how to bootstrap a ResIN network to derive uncertainty estimates around core quantities of interest. Requires output of ResIN function.

Usage

ResIN_boots_prepare(
  ResIN_object,
  n = 10000,
  boots_type = "resample",
  resample_size = NULL,
  weights = NULL,
  save_input = FALSE,
  seed_boots = 42
)

Arguments

ResIN_object	A ResIN object to prepare bootstrapping workflow.
n	Bootstrapping sample size. Defaults to 10.000.
boots_type	What kind of bootstrapping should be performed? If set to "resample", function performs row-wise re-sampling of raw data (useful for e.g., sensitivity or power analysis). If set to "permute", function will randomly reshuffle raw item responses (useful e.g., for simulating null-hypothesis distributions). Defaults to "resample".
resample_size	Optional parameter determining sample size when boots_type is set to "resample". Defaults of to number of rows in raw data.
weights	An optional weights vector that can be used to adjust the re-sampling of observations. Should either be NULL (default) or a positive numeric vector of the same length as the original data.
save_input	Should all input information for each bootstrap iteration (including re-sampled/permuted data) be stored. Set to FALSE by default to save a lot of memory and disk storage.
seed_boots	Random seed for bootstrap samples

Value

An object of class "ResIN_boots_prepped" containing a bootstrap plan (specification) used by ResIN_boots_execute. Use print(), summary(), length(), and [ to inspect or subset the plan. See ResIN_boots_prepped for details.

Examples

## Load the 12-item simulated Likert-type toy dataset
data(lik_data)

# Apply the ResIN function to toy Likert data:
ResIN_obj <- ResIN(lik_data, network_stats = TRUE,
                      generate_ggplot = FALSE, plot_ggplot = FALSE)


# Prepare for bootstrapping
prepped_boots <- ResIN_boots_prepare(ResIN_obj, n=100, boots_type="resample")

# Execute the prepared bootstrap list
executed_boots <-  ResIN_boots_execute(prepped_boots, parallel = TRUE, detect_cores = TRUE)

# Extract results - here for example, the network (global)-clustering coefficient
ResIN_boots_extract(executed_boots, what = "global_clustering", summarize_results = TRUE)

ResIN documentation built on March 2, 2026, 9:07 a.m.