## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

## ----setup--------------------------------------------------------------------
library(EDI)
set.seed(20260916)

n = 100
X = data.frame(
  baseline_score = round(rnorm(n, 5, 1.5), 1),
  female         = rbinom(n, 1, 0.5)
)
true_effect = 0.8   # shift on the latent logistic scale

## ----fixed--------------------------------------------------------------------
des = DesignFixedBernoulli$new(n = n, response_type = "ordinal", verbose = FALSE)
des$add_all_subjects_to_experiment(X)
des$assign_w_to_all_subjects()
w = des$get_w()

eta = true_effect * w + 0.3 * (X$baseline_score - 5) - 0.2 * X$female
u   = runif(n)
y   = ifelse(u <= plogis(-1.0 - eta), 1L,
      ifelse(u <= plogis( 0.2 - eta), 2L,
      ifelse(u <= plogis( 1.1 - eta), 3L, 4L)))
des$add_all_subject_responses(y)
table(level = y, treatment = w)

inf = InferenceOrdinalPropOddsRegr$new(des, verbose = FALSE)
inf$num_cores = 1L
inf$compute_estimate()                         # treatment log-odds shift
inf$compute_asymp_confidence_interval(alpha = 0.05)
inf$compute_asymp_two_sided_pval()

## ----fixed-resampling---------------------------------------------------------
inf$set_seed(1)
inf$compute_rand_two_sided_pval(r = 200, show_progress = FALSE)
inf$set_seed(1)
inf$compute_bootstrap_confidence_interval(alpha = 0.05, B = 200, show_progress = FALSE)

## ----suite--------------------------------------------------------------------
suite = InferenceSuite$new(des)
res = suite$run_all_inference(screen = TRUE, plots = FALSE, num_cores = 1L,
                              methods = c("wald", "score", "lik_ratio"), max_secs_per_class = 15)

## ----seq----------------------------------------------------------------------
des_seq = DesignSeqOneByOneKK14$new(n = n, response_type = "ordinal", verbose = FALSE)
for (i in seq_len(n)) {
  w_i   = des_seq$add_one_subject_to_experiment_and_assign(X[i, , drop = FALSE])
  eta_i = true_effect * w_i + 0.3 * (X$baseline_score[i] - 5) - 0.2 * X$female[i]
  u_i   = runif(1)
  y_i   = if (u_i <= plogis(-1.0 - eta_i)) 1L else if (u_i <= plogis(0.2 - eta_i)) 2L else
          if (u_i <= plogis(1.1 - eta_i)) 3L else 4L
  des_seq$add_one_subject_response(i, y_i)
}

inf_seq = InferenceOrdinalPropOddsRegr$new(des_seq, verbose = FALSE)
inf_seq$num_cores = 1L
inf_seq$compute_estimate()
inf_seq$set_seed(1)
inf_seq$compute_rand_two_sided_pval(r = 200, show_progress = FALSE)