Sensitivity to quantization choice in qgcompmulti fits

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

Usage

q_sensitivity(
  f,
  data,
  mix1,
  mix2,
  q_values,
  interaction = TRUE,
  family = gaussian(),
  centering = "none",
  B = 200,
  id = NULL,
  MCsize = nrow(data),
  seed = NULL,
  keep_fits = TRUE
)

Arguments

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

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

q_values

Integer vector of quantization choices to compare.

keep_fits

Logical; if TRUE, retain the full fitted objects.

Value

An object of class "qgcompmulti_q_sensitivity".

Details

This helper is intended for robustness assessment, not for coefficient ranking across different quantization choices.

q sensitivity is implemented as a repeated-fit workflow in which all settings other than q are held fixed.

Users should be cautious when comparing raw coefficient magnitudes across different values of q. A larger q implies a smaller one-quantile intervention step, so smaller coefficients may be expected mechanically even when the broader qualitative pattern of the fitted surface is stable. For that reason, the printed sensitivity object includes an explicit comparability note.

The helper therefore supports sensitivity assessment, not a claim that one choice of q produces “stronger” or “weaker” effects based only on raw one-step coefficient magnitudes.

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
)

q_sensitivity(
  f = Y ~ X1 + X2 + X3 + W1 + W2 + W3 + C,
  data = dat,
  mix1 = c("X1", "X2", "X3"),
  mix2 = c("W1", "W2", "W3"),
  q_values = c(3, 4, 5),
  B = 100,
  seed = 13
)
} # }