RMlocdepQ3Cutoff: Simulation-Based Q_3 Cutoff Determination

In easyRasch2: Psychometric Analysis with Rasch Measurement Theory

Simulation-Based Q_3 Cutoff Determination

Description

Uses parametric bootstrap simulation to determine an appropriate cutoff value for RMlocdepQ3. Under a correctly fitting Rasch model, Q_3 residuals have an unknown distribution; this function simulates that distribution and returns empirical percentiles.

Usage

RMlocdepQ3Cutoff(
  data,
  iterations = 400,
  parallel = TRUE,
  n_cores = NULL,
  verbose = FALSE,
  seed = NULL,
  cutoff_method = "hdci",
  hdci_width = 0.95,
  estimator = c("CML", "MML"),
  dgp = c("resample", "conditional")
)

Arguments

data	A data.frame or matrix of item responses. Items must be scored starting at 0 (non-negative integers).
iterations	Integer. Number of simulation iterations (default 400). 400 is the calibrated floor for the Westfall-Young correction (Johansson, 2026) and the count a 95\ 1000 to 2000 for a final analysis.
parallel	Logical. Use parallel processing via mirai if available (default TRUE).
n_cores	Integer or NULL. Number of parallel workers. When NULL, getOption("mc.cores") is checked first. If neither is set and parallel = TRUE, a warning is issued and execution falls back to sequential (single core) processing.
verbose	Logical. Show a progress bar (default FALSE).
seed	Integer or NULL. Random seed for reproducibility. See easyRasch2-reproducibility for what this guarantees and how it interacts with parallel.
cutoff_method	Character. Method used to compute per-pair Q_3 credible intervals in pair_cutoffs. One of "hdci" (the default, Highest Density Continuous Interval via ggdist::hdci()) or "quantile" (symmetric 2.5th / 97.5th percentiles). Only affects pair_cutoffs; the global ⁠$suggested_cutoff⁠ (99th percentile of max(Q3) - mean(Q3)) is unaffected.
hdci_width	Numeric in (0, 1). Width of the HDCI when cutoff_method = "hdci". Default 0.95, was 0.99 before 1.2.0. The interval describes where a fitting pair's Q_3 is expected to fall and is no longer the default decision rule, so the width is chosen to converge at the default iteration count rather than to imply an error rate. Ignored when cutoff_method = "quantile".
estimator	Character. Estimation engine for the simulated Q_3 values, passed through to the per-iteration computation. "CML" (default) uses CML item parameters and WLE person locations; "MML" uses mirt. This must match the estimator later given to RMlocdepQ3; the value is stored in the returned object's ⁠$estimator⁠ and reused automatically.
dgp	Character. Data-generating process for the parametric bootstrap. "resample" (default) draws person locations by resampling the WLE estimates with replacement and simulates responses under the model – a marginal null. "conditional" instead simulates each respondent's pattern from the exact Rasch conditional distribution given their observed total score (and answered items), with item parameters fixed – a conditional null that fixes the score margin and needs no latent distribution, avoiding the over-dispersion of resampled point estimates. The two give different cut-offs; see the package's comparison study. Experimental.

Details

The generating model is fitted once: CML item parameters (via psychotools) and WLE person locations. For each simulation iteration, those WLE thetas are resampled with replacement, response data are simulated under the Rasch / Partial Credit model, the model is refitted, and Q_3 residuals are computed under estimator. The distribution of max(Q3) - mean(Q3) across iterations provides empirical critical values. Failed iterations (e.g., due to convergence issues) are silently discarded.

Supports both dichotomous data (simulated via psychotools::rrm()) and polytomous data (via an internal partial credit score simulator).

Parallel processing is provided by the mirai package (optional). Install it with install.packages("mirai") to enable parallelisation.

Value

A list with components:

results

data.frame with columns mean, max, diff (one row per successful iteration).

pair_results

Long data.frame with columns Item1, Item2, Q3, iteration — one row per item pair per successful iteration. Used by RMlocdepQ3Plot.

pair_cutoffs

data.frame with per-pair cutoff summaries: Item1, Item2, Q3_low, Q3_high. Boundaries are computed via the method specified by cutoff_method.

actual_iterations

Number of successful iterations.

sample_n

Number of persons used: respondents with no responses at all (all-NA rows) are dropped, as in RMlocdepQ3; incomplete response patterns are retained.

sample_n_total

Number of respondents in the raw input data, before dropping all-NA rows.

sample_has_na

Logical. Whether the data contained any missing values.

sample_summary

Summary statistics of estimated person parameters.

item_names

Character vector of item names from data.

max_diff, sd_diff

Max and SD of the diff distribution.

p95, p99, p995, p999

Empirical percentiles of diff.

suggested_cutoff

The 99th percentile (p99) — recommended scalar cutoff for RMlocdepQ3.

cutoff_method

The method used for pair_cutoffs ("hdci" or "quantile").

hdci_width

The HDCI width used (only meaningful when cutoff_method = "hdci").

estimator

The estimator used for the simulated Q_3 ("CML" or "MML"); reused by RMlocdepQ3 and RMlocdepQ3Plot.

dgp

The data-generating process used ("resample" or "conditional").

See Also

RMlocdepQ3

Examples

if (requireNamespace("ggdist", quietly = TRUE)) {
  set.seed(42)
  sim_data <- as.data.frame(
    matrix(sample(0:1, 200 * 10, replace = TRUE), nrow = 200, ncol = 10)
  )
  colnames(sim_data) <- paste0("Item", 1:10)

  # Few iterations for a fast example; use 500+ in real analyses
  cutoff_res <- RMlocdepQ3Cutoff(sim_data, iterations = 50, parallel = FALSE,
                                 seed = 42)
  cutoff_res$suggested_cutoff  # 99th percentile

  # Use the cutoff in RMlocdepQ3()
  RMlocdepQ3(sim_data, cutoff = cutoff_res$suggested_cutoff)
}

easyRasch2 documentation built on Sept. 13, 2026, 1:07 a.m.