Abstract class for ordinal CLMM-based Inference in KK designs

Abstract class for ordinal CLMM-based Inference in KK designs

Super class

Inference -> InferenceAbstractKKOrdinalCLMM

Methods

Public methods

• InferenceAbstractKKOrdinalCLMM$new()

• InferenceAbstractKKOrdinalCLMM$compute_estimate()

• InferenceAbstractKKOrdinalCLMM$compute_estimate_with_bootstrap_weights()

• InferenceAbstractKKOrdinalCLMM$compute_asymp_confidence_interval()

• InferenceAbstractKKOrdinalCLMM$compute_asymp_two_sided_pval()

• InferenceAbstractKKOrdinalCLMM$clone()

+ 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()

---

InferenceAbstractKKOrdinalCLMM$new()

Initialize KK cumulative-link mixed-model inference for ordinal responses, validate the matched design, and prepare the ordinal likelihood used by InferenceAbstractKKOrdinalCLMM.

Usage

InferenceAbstractKKOrdinalCLMM$new(
  des_obj,
  model_formula = NULL,
  use_rcpp = TRUE,
  verbose = FALSE,
  harden = TRUE,
  smart_cold_start_default = NULL
)

Arguments

des_obj

A completed Design object.

model_formula

Optional formula for covariate adjustment.

use_rcpp

Logical. If TRUE (default), use the internal Rcpp implementation (no external packages required). Set FALSE to fall back to ordinal::clmm.

verbose

A flag indicating whether messages should be displayed.

harden

Whether to apply robustness measures.

smart_cold_start_default

Whether to use smart cold start values.

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InferenceAbstractKKOrdinalCLMM$compute_estimate()

Compute the ordinal CLMM treatment-effect estimate by fitting the cumulative-link mixed model and caching the treatment coefficient for related InferenceAsymp methods.

Usage

InferenceAbstractKKOrdinalCLMM$compute_estimate(estimate_only = FALSE)

Arguments

estimate_only

Logical. If TRUE, skip variance component calculations.

---

InferenceAbstractKKOrdinalCLMM$compute_estimate_with_bootstrap_weights()

Recomputes the KK ordinal CLMM treatment estimate under Bayesian-bootstrap weights.

Usage

InferenceAbstractKKOrdinalCLMM$compute_estimate_with_bootstrap_weights(
  subject_or_block_weights,
  estimate_only = FALSE
)

Arguments

subject_or_block_weights

Numeric vector. Row weights for bootstrap.

estimate_only

Logical. If TRUE, skip variance component calculations.

---

InferenceAbstractKKOrdinalCLMM$compute_asymp_confidence_interval()

Compute the ordinal CLMM asymptotic confidence interval for the treatment coefficient using the fitted-model standard error. See InferenceAsymp.

Usage

InferenceAbstractKKOrdinalCLMM$compute_asymp_confidence_interval(alpha = 0.05)

Arguments

alpha

Numeric. Significance level (default 0.05).

---

InferenceAbstractKKOrdinalCLMM$compute_asymp_two_sided_pval()

Compute the ordinal CLMM asymptotic two-sided p-value for the treatment coefficient using the fitted-model standard error. See InferenceAsymp.

Usage

InferenceAbstractKKOrdinalCLMM$compute_asymp_two_sided_pval(delta = 0)

Arguments

delta

Numeric. Null treatment effect value (default 0).

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InferenceAbstractKKOrdinalCLMM$clone()

The objects of this class are cloneable with this method.

Usage

InferenceAbstractKKOrdinalCLMM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.