Conditional Logistic Plus GLMM IVWC Inference for KK Designs
Source:R/inference_incidence_KK_cond_logit_glmm.R
InferenceIncidKKCondLogitGLMMIVWC.RdFits a combined conditional-logit-plus-random-intercept-GLMM likelihood for
incidence responses under a KK matching-on-the-fly design, where
reservoir (unmatched) subjects are excluded from the GLMM
component (private$combine_reservoir_into_glmm() == FALSE): only
concordant matched pairs contribute their random-intercept GLMM likelihood
alongside the discordant-pair conditional-logit term, both sharing a single
treatment coefficient \(\beta_T\). See
InferencePropKKGLMM for the full
model form (conditional-logit-on-discordant plus random-intercept-GLMM,
jointly maximized) and
InferenceAbstractKKCondLogitGLMM
for the shared fitting/caching contract. Contrast with the sibling
InferenceIncidKKCondLogitGLMMOneLik,
which instead includes reservoir subjects in the GLMM component
(combine_reservoir_into_glmm() == TRUE) — this class's naming
("IVWC") reflects that reservoir information, when used, is intended to be
combined with this fit's estimate via inverse-variance weighting rather
than folded into the same likelihood.
Details
Legacy class. Not fully tested in comprehensive_tests.R.
See InferenceAbstractKKCondLogitGLMM's
known estimability limitation at high covariate counts, which applies here
too.
Super classes
Inference -> InferenceAbstractKKCondLogitGLMM -> InferenceIncidKKCondLogitGLMMIVWC
Methods
+ inherited public methods from InferenceAbstractKKCondLogitGLMM
InferenceAbstractKKCondLogitGLMM$approximate_bayesian_bootstrap_distribution_beta_hat_T()InferenceAbstractKKCondLogitGLMM$approximate_bootstrap_distribution_beta_hat_T()InferenceAbstractKKCondLogitGLMM$approximate_jackknife_distribution_beta_hat_T()InferenceAbstractKKCondLogitGLMM$approximate_m_out_of_n_bootstrap_distribution_beta_hat_T()InferenceAbstractKKCondLogitGLMM$approximate_rand_bootstrap_distribution_beta_hat_T()InferenceAbstractKKCondLogitGLMM$approximate_randomization_distribution_beta_hat_T()InferenceAbstractKKCondLogitGLMM$approximate_subsampling_distribution_beta_hat_T()InferenceAbstractKKCondLogitGLMM$compute_asymp_confidence_interval()InferenceAbstractKKCondLogitGLMM$compute_asymp_two_sided_pval()InferenceAbstractKKCondLogitGLMM$compute_bayesian_bootstrap_confidence_interval()InferenceAbstractKKCondLogitGLMM$compute_bayesian_bootstrap_two_sided_pval()InferenceAbstractKKCondLogitGLMM$compute_bootstrap_confidence_interval()InferenceAbstractKKCondLogitGLMM$compute_bootstrap_two_sided_pval()InferenceAbstractKKCondLogitGLMM$compute_estimate()InferenceAbstractKKCondLogitGLMM$compute_estimate_with_bootstrap_weights()InferenceAbstractKKCondLogitGLMM$compute_gradient_confidence_interval()InferenceAbstractKKCondLogitGLMM$compute_gradient_two_sided_pval()InferenceAbstractKKCondLogitGLMM$compute_jackknife_bias_estimate()InferenceAbstractKKCondLogitGLMM$compute_jackknife_estimate()InferenceAbstractKKCondLogitGLMM$compute_jackknife_std_error()InferenceAbstractKKCondLogitGLMM$compute_jackknife_wald_confidence_interval()InferenceAbstractKKCondLogitGLMM$compute_jackknife_wald_two_sided_pval()InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_bartlett_approx_confidence_interval()InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_bartlett_approx_two_sided_pval()InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_bartlett_confidence_interval()InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_bartlett_exact_confidence_interval()InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_bartlett_exact_two_sided_pval()InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_bartlett_two_sided_pval()InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_bootstrap_confidence_interval()InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_bootstrap_two_sided_pval()InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_confidence_interval()InferenceAbstractKKCondLogitGLMM$compute_lik_ratio_two_sided_pval()InferenceAbstractKKCondLogitGLMM$compute_m_out_of_n_bootstrap_confidence_interval()InferenceAbstractKKCondLogitGLMM$compute_m_out_of_n_bootstrap_two_sided_pval()InferenceAbstractKKCondLogitGLMM$compute_param_bootstrap_confidence_interval()InferenceAbstractKKCondLogitGLMM$compute_param_bootstrap_estimate()InferenceAbstractKKCondLogitGLMM$compute_param_bootstrap_pval()InferenceAbstractKKCondLogitGLMM$compute_rand_bootstrap_confidence_interval()InferenceAbstractKKCondLogitGLMM$compute_rand_bootstrap_two_sided_pval()InferenceAbstractKKCondLogitGLMM$compute_rand_confidence_interval()InferenceAbstractKKCondLogitGLMM$compute_rand_two_sided_pval()InferenceAbstractKKCondLogitGLMM$compute_score_confidence_interval()InferenceAbstractKKCondLogitGLMM$compute_score_two_sided_pval()InferenceAbstractKKCondLogitGLMM$compute_subsampling_confidence_interval()InferenceAbstractKKCondLogitGLMM$compute_subsampling_sensitivity()InferenceAbstractKKCondLogitGLMM$compute_subsampling_two_sided_pval()InferenceAbstractKKCondLogitGLMM$compute_wald_confidence_interval()InferenceAbstractKKCondLogitGLMM$compute_wald_two_sided_pval()InferenceAbstractKKCondLogitGLMM$get_information_preference()InferenceAbstractKKCondLogitGLMM$get_information_source_used()InferenceAbstractKKCondLogitGLMM$get_last_param_bootstrap_diagnostics()InferenceAbstractKKCondLogitGLMM$get_last_param_bootstrap_estimate_diagnostics()InferenceAbstractKKCondLogitGLMM$get_mod()InferenceAbstractKKCondLogitGLMM$get_summary()InferenceAbstractKKCondLogitGLMM$get_supported_bayesian_bootstrap_ci_types()InferenceAbstractKKCondLogitGLMM$get_supported_bayesian_bootstrap_pval_types()InferenceAbstractKKCondLogitGLMM$get_supported_bootstrap_ci_types()InferenceAbstractKKCondLogitGLMM$get_supported_bootstrap_pval_types()InferenceAbstractKKCondLogitGLMM$get_supported_information_preferences()InferenceAbstractKKCondLogitGLMM$get_supported_rand_bootstrap_ci_types()InferenceAbstractKKCondLogitGLMM$get_supported_rand_bootstrap_pval_types()InferenceAbstractKKCondLogitGLMM$get_supported_testing_types()InferenceAbstractKKCondLogitGLMM$get_testing_type()InferenceAbstractKKCondLogitGLMM$initialize()InferenceAbstractKKCondLogitGLMM$select_optimal_b_subsampling()InferenceAbstractKKCondLogitGLMM$select_optimal_m_out_of_n_bootstrap()InferenceAbstractKKCondLogitGLMM$set_information_preference()InferenceAbstractKKCondLogitGLMM$set_testing_type()InferenceAbstractKKCondLogitGLMM$supports_rand_pval_for_incidence()+ inherited public methods from Inference
Inference$capabilities()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$get_treatment()Inference$is_nonestimable()Inference$set_optimization_alg()Inference$set_seed()Inference$supports()
Examples
seq_des = DesignSeqOneByOneKK14$new(n = 10, response_type = 'incidence')
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(rbinom(10, 1, 0.5))
inf = InferenceIncidKKCondLogitGLMMIVWC$new(seq_des)
inf$compute_estimate()
#> [1] -1.279993