ResIN_boots_prepare — ResIN_boots

Prepare a ResIN-based bootstrap analysis

Usage

ResIN_boots_prepare(
  ResIN_object,
  n = 10000,
  boots_type = "resample",
  resample_size = NULL,
  weights = NULL,
  save_input = FALSE,
  seed = 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: Random seed for bootstrap samples

Value

A list object containing n re-sampled or permuted copies of the raw data, along with a list of instructions for how to perform the ResIN analysis and what outputs to generate.

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, cor_method = "spearman", network_stats = TRUE,
                      generate_ggplot = FALSE)
#> [1] "not generated"

if (FALSE) { # \dontrun{
# Prepare for bootstrapping
prepped_boots <- ResIN_boots_prepare(ResIN_obj, n=5000, boots_type="permute")

# 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)
} # }