Continuation Ratio Regression Inference for Ordinal Responses

Fits a conditional (stratified) continuation-ratio logit model for ordinal responses: for cut \(j = 1, \dots, K-1\), among subjects who have reached at least category \(j\), $$\log\frac{\Pr(Y_i > j \mid Y_i \ge j)}{\Pr(Y_i = j \mid Y_i \ge j)} = \alpha_j + \beta_T W_i + X_i^\top \gamma,$$ a discrete-time-hazard-model analog for ordinal data, with a treatment coefficient \(\beta_T\) constrained equal across all cuts. \(\exp(\hat\beta_T)\) is the common "continue vs. stop here" odds ratio: a positive \(\beta_T\) means treatment pushes subjects toward higher categories of \(Y\), matching the sign convention of every other ordinal estimator in the package. Fitting proceeds by expand_continuation_ratio_data_cpp's stacked-binary expansion followed by conditional logistic regression on the expanded data. likelihood_tier = "full": likelihood-ratio, score, gradient, and Wald tests are all available when the model converges, plus parametric-likelihood-bootstrap calibration of the likelihood-ratio test. Validity requires the continuation-ratio proportionality assumption (a common \(\beta_T\) across all \(K-1\) cuts).

References

Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). Wiley, for the continuation-ratio model family.

See also

InferenceOrdinalStereotypeLogitRegr and InferenceOrdinalKKCondAdjCatLogitRegr for related ordinal-logit expansions. See also: Ordinal regression (Wikipedia).

Super class

Inference -> InferenceOrdinalContRatioRegr

Methods

Public methods

• InferenceOrdinalContRatioRegr$set_custom_randomization_statistic_function()

• InferenceOrdinalContRatioRegr$set_custom_randomization_statistic_cpp()

• InferenceOrdinalContRatioRegr$approximate_randomization_distribution_beta_hat_T()

• InferenceOrdinalContRatioRegr$supports_rand_pval_for_incidence()

• InferenceOrdinalContRatioRegr$compute_rand_two_sided_pval()

• InferenceOrdinalContRatioRegr$clone()

+ 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()

---

InferenceOrdinalContRatioRegr$set_custom_randomization_statistic_function()

Usage

InferenceOrdinalContRatioRegr$set_custom_randomization_statistic_function(
  custom_randomization_statistic_function
)

---

InferenceOrdinalContRatioRegr$set_custom_randomization_statistic_cpp()

Usage

InferenceOrdinalContRatioRegr$set_custom_randomization_statistic_cpp(fn)

---

InferenceOrdinalContRatioRegr$approximate_randomization_distribution_beta_hat_T()

Usage

InferenceOrdinalContRatioRegr$approximate_randomization_distribution_beta_hat_T(
  r = 501,
  delta = 0,
  transform_responses = "none",
  show_progress = TRUE,
  permutations = NULL,
  debug = FALSE,
  zero_one_logit_clamp = .Machine$double.eps
)

---

InferenceOrdinalContRatioRegr$supports_rand_pval_for_incidence()

Usage

InferenceOrdinalContRatioRegr$supports_rand_pval_for_incidence()

---

InferenceOrdinalContRatioRegr$compute_rand_two_sided_pval()

Usage

InferenceOrdinalContRatioRegr$compute_rand_two_sided_pval(
  r = 501,
  delta = 0,
  transform_responses = "none",
  na.rm = TRUE,
  show_progress = TRUE,
  permutations = NULL,
  zero_one_logit_clamp = .Machine$double.eps
)

---

InferenceOrdinalContRatioRegr$clone()

The objects of this class are cloneable with this method.

Usage

InferenceOrdinalContRatioRegr$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# \donttest{
seq_des = DesignSeqOneByOneBernoulli$new(n = 10, response_type = 'ordinal')
for (i in 1:10) {
  seq_des$add_one_subject_to_experiment_and_assign(data.frame(x1 = rnorm(1)))
}
seq_des$add_all_subject_responses(sample(1:4, 10, replace = TRUE))
inf = InferenceOrdinalContRatioRegr$new(seq_des)
inf$compute_estimate()
#> [1] -0.01547521
# }