OLS IVWC Compound Inference for KK Designs
Source:R/inference_continuous_KK_ols_ivwc.R
InferenceContinKKOLSIVWC.RdFits a variance-weighted compound estimator for KK matching-on-the-fly designs with continuous responses using OLS regression for matched-pair differences and reservoir outcomes, with the treatment indicator and, optionally, all recorded covariates as predictors. Note that warm starts are disabled for this class as OLS is a closed-form estimator and does not benefit from initialization.
Computes the randomization distribution of the treatment effect estimate under the sharp null.
Whether compute_rand_two_sided_pval() is actually
usable on this instance right now – FALSE exactly when it
would stop(): an incidence-response instance with no
custom randomization statistic and a design not eligible for
design-randomization-based incidence inference (see
private$should_use_design_randomization_for_incidence()).
TRUE for every other case, including every non-incidence
response type. Public, self-contained (only reads already-set
instance state, no side effects), so InferenceSuite can
check this before attempting the sentinel instead of relying on
the stop() being silently swallowed into a pval = NA
"ok" row – the single source of truth for both this check and
compute_rand_two_sided_pval()'s own guard, so the two can
never drift apart.
Computes a randomization-based p-value.
Value
When debug = FALSE (default), a numeric vector of length r. When
debug = TRUE, a list with: values, errors (list of character
vectors, one per iteration), warnings (list of character vectors, one per
iteration), num_errors, num_warnings,
prop_iterations_with_errors, prop_iterations_with_warnings, and
prop_illegal_values.
A single logical.
Randomization p-value.
Details
The point estimate \(\hat\beta_T\) is the inverse-variance-weighted
combination of an OLS fit on matched-pair within-pair differences and an
OLS fit on reservoir (unmatched) subjects' outcomes, falling back to
whichever sub-fit is usable if the other is not — the same compound
combination rule used by
InferenceAllKKMeanDiffIVWC,
generalized here to allow covariate adjustment via model_formula.
likelihood_tier = "none": this is an estimating-equation (least
squares) estimator, not a fitted likelihood, so only Wald-type asymptotic
inference is available (no likelihood-ratio or score test).
Legacy class. Not fully tested in comprehensive_tests.R.
Super class
Inference -> InferenceContinKKOLSIVWC
Methods
+ inherited public methods from Inference
Inference$capabilities()Inference$compute_asymp_confidence_interval()Inference$compute_asymp_two_sided_pval()Inference$compute_estimate()Inference$compute_exact_confidence_interval()Inference$compute_exact_two_sided_pval_for_treatment_effect()Inference$duplicate()Inference$get_analysis_data()Inference$get_covariates()Inference$get_design_object()Inference$get_model_formula()Inference$get_nonestimable_reason()Inference$get_nonestimable_stage()Inference$get_optimization_alg()Inference$get_response()Inference$get_response_type()Inference$get_treatment()Inference$initialize()Inference$is_nonestimable()Inference$set_optimization_alg()Inference$set_seed()Inference$supports()
InferenceContinKKOLSIVWC$approximate_randomization_distribution_beta_hat_T()
Usage
InferenceContinKKOLSIVWC$approximate_randomization_distribution_beta_hat_T(
r = 501,
delta = 0,
transform_responses = "none",
show_progress = TRUE,
permutations = NULL,
debug = FALSE,
zero_one_logit_clamp = .Machine$double.eps
)Arguments
rNumber of randomization vectors. Default 501.
rNumber of randomization vectors.
deltaThe null difference. Default 0.
deltaNull difference.
transform_responsesType of transformation. Default "none".
transform_responsesTransformation.
show_progressShow progress bar. Default TRUE.
show_progressShow progress.
permutationsPre-computed permutations. Default NULL.
permutationsPre-computed permutations.
debugIf
TRUE, return a list with the distribution values and per-iteration diagnostics including error messages, warning messages, counts of each, and summary proportions for iterations with errors, warnings, and illegal (non-finite) values. Runs serially. DefaultFALSE.zero_one_logit_clampThe clamping amount for exact 0 and 1 values when logging
zero_one_logit_clampThe clamping amount for exact 0 and 1 values when logging
InferenceContinKKOLSIVWC$compute_rand_two_sided_pval()
Usage
InferenceContinKKOLSIVWC$compute_rand_two_sided_pval(
r = 501,
delta = 0,
transform_responses = "none",
na.rm = TRUE,
show_progress = TRUE,
permutations = NULL,
zero_one_logit_clamp = .Machine$double.eps
)Arguments
rNumber of randomization vectors. Default 501.
rNumber of randomization vectors.
deltaThe null difference. Default 0.
deltaNull difference.
transform_responsesType of transformation. Default "none".
transform_responsesTransformation.
na.rmRemove NAs.
show_progressShow progress bar. Default TRUE.
show_progressShow progress.
permutationsPre-computed permutations. Default NULL.
permutationsPre-computed permutations.
zero_one_logit_clampThe clamping amount for exact 0 and 1 values when logging
zero_one_logit_clampThe clamping amount for exact 0 and 1 values when logging
Examples
# \donttest{
seq_des = DesignSeqOneByOneKK14$new(n = 10, response_type = 'continuous')
for (i in 1:10) {
seq_des$add_one_subject_to_experiment_and_assign(data.frame(x1 = rnorm(1), x2 = rnorm(1)))
}
seq_des$add_all_subject_responses(rnorm(10))
inf = InferenceContinKKOLSIVWC$new(seq_des)
inf$compute_estimate()
#> [1] 1.444449
# }