Parametric bootstrap inference for a VCMoE fit

Runs parametric bootstrap inference for a fitted Gaussian, Binomial, or Negative-Binomial VCMoE model with k = 2:10.

Usage

vcmoe_bootstrap(
  fit,
  data,
  u = NULL,
  B = 200L,
  coefficient_set = c("expert", "gating"),
  seed = NULL,
  control = list(),
  min_successful = max(20L, ceiling(0.5 * B)),
  keep_fits = FALSE,
  verbose = FALSE
)

Arguments

fit

A fitted vcmoe object. Bootstrap inference supports k = 2:10.

data

Original data frame used to fit fit. The function resamples from data[fit$rows_used, ].

u

Optional original u values or column name. If NULL, the stored u column from fit is reused when available.

B

Number of parametric bootstrap replicates.

coefficient_set

Coefficient sets to store and summarize: "expert", "gating", or both.

seed

Optional random seed.

control

Named list passed to bootstrap refits. Bandwidth is not reselected inside bootstrap v0.

min_successful

Minimum number of successful replicates expected for reliable inference. The object is returned when at least two replicates succeed, but a warning is recorded below this threshold.

keep_fits

Whether to store successful bootstrap fit objects.

verbose

Whether to print replicate progress messages.

Value

An object of class vcmoe_bootstrap with fields fit, replicates, replicate_summary, alignment_summary, settings, warnings, and optionally fits.

Details

For Gaussian fits, each bootstrap data set draws a latent component from the fitted gating probabilities and then draws the response from the selected component Normal distribution. For Binomial fits, each bootstrap data set draws success counts from the selected component success probability. For Negative-Binomial fits, each bootstrap data set draws counts from the selected component mean and theta. Bernoulli and grouped cbind(success, failure) response formats are preserved for Binomial fits.

Each bootstrap replicate is refit with the same formula, family, number of components, bandwidth, u_grid, and label strategy as the reference fit. After the usual within-grid label alignment, one global component permutation matches the bootstrap coefficient paths back to the reference fit. Ambiguous bootstrap-to-reference matches are recorded in alignment_summary. Exact permutation matching is used for small k; assignment-based matching is used when exhaustive permutation is infeasible.

Binomial expert coefficients and intervals are on the logit coefficient scale. Negative-Binomial expert coefficients and intervals are on the log mean count scale.