Sensitivity to Monte Carlo size in qgcompmulti fits

Re-fits qgcomp.glm.multi() across multiple MCsize values while preserving the rest of the analysis specification.

Usage

mcsize_sensitivity(
  f,
  data,
  mix1,
  mix2,
  MCsize_values,
  interaction = TRUE,
  family = gaussian(),
  q = 4,
  centering = "none",
  B = 200,
  id = NULL,
  seed = NULL,
  keep_fits = TRUE
)

Arguments

f, data, mix1, mix2, interaction, family, q, centering, B, id, seed

Arguments passed through to qgcomp.glm.multi().

MCsize_values

Integer vector of Monte Carlo sizes to compare.

keep_fits

Logical; if TRUE, retain the full fitted objects.

Value

An object of class "qgcompmulti_mcsize_sensitivity".

Details

This helper is intended to answer a practical computational question: are the fitted results reasonably stable as the Monte Carlo approximation size changes?

MCsize sensitivity is implemented as a repeated-fit workflow rather than a diagnostic of one existing fit. The helper keeps the model formula, outcome family, mixture definitions, interaction setting, quantization choice, bootstrap count, clustering, and seed fixed while varying only MCsize.

The resulting object is designed for stability assessment rather than automatic tuning. Users should look for whether the fitted coefficients, adequacy summaries, and bootstrap behavior are reasonably consistent across the requested MCsize values.

Examples

if (FALSE) { # \dontrun{
dat <- sim_mixture_data(
  n = 400,
  pA = 3,
  pB = 3,
  rho_within_A = 0.3,
  rho_within_B = 0.3,
  rho_between = 0.2,
  psi1 = 0.5,
  psi2 = 0.3,
  psi12 = 0.2,
  seed = 123
)

mcsize_sensitivity(
  f = Y ~ X1 + X2 + X3 + W1 + W2 + W3 + C,
  data = dat,
  mix1 = c("X1", "X2", "X3"),
  mix2 = c("W1", "W2", "W3"),
  MCsize_values = c(100, 200, 400),
  q = 4,
  B = 100,
  seed = 13
)
} # }