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Check integration point adequacy

Source: R/integration.R
check_integration.Rd

Compare integration results at the current n_int against a doubled resolution to assess numerical accuracy. Large discrepancies indicate that n_int should be increased. Because the Sobol sequence is nested (the doubled set contains the current set), this current-vs-doubled difference is a convergence heuristic, not an error bound. Agreement between the two grids does not establish accuracy for rare discrete margins or for a final treatment-effect estimand.

Usage

check_integration(
  data,
  ...,
  cor = NULL,
  cor_adjust = NULL,
  check_joint = TRUE,
  verbose = TRUE
)

Arguments

data

An mlumr_data object with integration points

...

Distribution specifications (same as passed to add_integration())

cor

Correlation matrix (same as passed to add_integration())

cor_adjust

Adjustment method (same as passed to add_integration())

check_joint

If TRUE (default), also compare pairwise correlation matrices between the current and doubled n_int, and the maximum per-AgD-row absolute deviation from the user-supplied cor. The pairwise comparison catches cases where marginals converge but joint dependence structure does not (rare in practice for QMC with sensible cor_adjust but worth flagging when n_int is small).

verbose

Logical; if FALSE, suppresses printed diagnostic messages.

Value

A list with marginals, a data frame of grid means and SDs at the current and doubled n_int against the declared targets, and verdict, whose entries are "stable" or "close" when a comparison met the heuristic, "review" when it did not, "partial" when the measured correlation pairs passed but some pair could not be measured, and "unavailable" when there was nothing finite to compare (a latent matrix under cor_adjust = "none" is never compared). With check_joint = TRUE and two or more covariates it also holds correlations, the pairwise correlations per AgD row on both grids, and correlation_pairs, which counts the pairs measured and names the rest. A binary margin's target SD is sqrt(p * (1 - p)) from the declared mean, and grid SDs are population SDs.