Bai Adjusted-t Mean-Difference Inference for KK21 Designs
Source:R/inference_continuous_KK21_bai.R
InferenceBaiAdjustedTKK21.RdContinuous-response mean-difference inference for designs assigned by
DesignSeqOneByOneKK21 (the
Kapelner-Krieger 2021 sequential matching-on-the-fly design with
covariate-weighted matching). The point estimate and its variance are the
closed-form Bai-adjusted-t combination of the matched-pairs mean
difference and the unmatched-reservoir mean difference,
inverse-variance-weighted when both are usable; see
InferenceBaiAdjustedTKK14 for
the full formula, the pair-distance definition, and the
confidence-interval/p-value construction shared with this class.
The two leaves differ only in how pair distance is defined during
matching: this class (KK21) uses the design's covariate-weighted squared
distance \(\sum_j w_j (x_{1j} - x_{2j})^2\), where \(w_j\) are the
design's covariate_weights (see
DesignSeqOneByOneKK21), unlike
KK14's unweighted distance. Because the estimator is closed-form,
initialization does not use warm starts (there is no iterative fit to
warm-start).
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$should_use_design_randomization_for_incidence()).
TRUE for every other case, including every non-incidence
response type. Public, self-contained (only reads already-set
instance state, no side effects), so InferenceSuite can
check this before attempting the sentinel instead of relying on
the stop() being silently swallowed into a pval = NA
"ok" row – the single source of truth for both this check and
compute_rand_two_sided_pval()'s own guard, so the two can
never drift apart.
Computes a randomization-based p-value.
Value
When debug = FALSE (default), a numeric vector of length r. When
debug = TRUE, a list with: values, errors (list of character
vectors, one per iteration), warnings (list of character vectors, one per
iteration), num_errors, num_warnings,
prop_iterations_with_errors, prop_iterations_with_warnings, and
prop_illegal_values.
A single logical.
Randomization p-value.
Super class
Inference -> InferenceBaiAdjustedTKK21
Methods
+ inherited public methods from Inference
Inference$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$supports()
InferenceBaiAdjustedTKK21$approximate_randomization_distribution_beta_hat_T()
Usage
InferenceBaiAdjustedTKK21$approximate_randomization_distribution_beta_hat_T(
r = 501,
delta = 0,
transform_responses = "none",
show_progress = TRUE,
permutations = NULL,
debug = FALSE,
zero_one_logit_clamp = .Machine$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
InferenceBaiAdjustedTKK21$compute_rand_two_sided_pval()
Usage
InferenceBaiAdjustedTKK21$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 = InferenceBaiAdjustedTKK21$new(seq_des)
inf$compute_estimate()
#> [1] 0.4387771
# }