11 dummy data (#15)

* add dummy data

* rename dummy data

R/data.R

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+#' Dummy Trial Data with Permute-Block Randomization
+#'
+#' This dataset contains the dummy trial data with permute block randomization.
+#'
+#' @format A data frame with 400 rows and 3 columns:
+#'   \describe{
+#'     \item{id}{The ID of the patients.}
+#'     \item{treatment}{The treatment assignment.}
+#'     \item{s1}{The first stratification variable.}
+#'     \item{s2}{The second stratification variable.}
+#'     \item{covar}{The covariate.}
+#'     \item{y}{The continuous response.}
+#'     \item{y_b}{The binary response.}
+#'   }
+#'
+#' @source The data is generated by the create_dummy.R script.
+#'
+#' @keywords dataset
+"dummy_data"

data-raw/create_dummy.R

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+#' Create permute-block randomization dummy data
+
+library(dplyr)
+#' Set seed to ensure reproducibility
+set.seed(20240408)
+
+n <- 400
+block <- c(0L, 0L, 1L, 1L)
+
+dummy_data <- tibble(
+  s1 = sample(c("a", "b"), replace = TRUE, size = n),
+  s2 = sample(c("c", "d"), replace = TRUE, size = n),
+  covar = rnorm(n),
+  id = seq_len(n)
+) %>%
+  group_by(s1, s2) %>%
+  mutate(treatment = unlist(replicate(ceiling(n() / length(block)), sample(block)))[seq_len(n())]) %>%
+  ungroup() %>%
+  mutate(
+    y = covar * 0.2 + 0.4 * (s1 == "a") - 0.1 * (s2 == "c") + 0.6 * treatment + rnorm(n),
+    y_b = ifelse(y > 0.6, 1L, 0L)
+  ) %>%
+  mutate(
+    s1 = factor(s1),
+    s2 = factor(s2)
+  ) %>%
+  select(id, treatment, s1, s2, covar, y, y_b)
+
+usethis::use_data(dummy_data)