pkgdown

NAMESPACE

@@ -12,6 +12,7 @@ S3method(brm_has_nuisance,brms_mmrm_archetype)
 S3method(brm_has_nuisance,brms_mmrm_data)
 S3method(brm_has_subgroup,brms_mmrm_archetype)
 S3method(brm_has_subgroup,brms_mmrm_data)
+S3method(print,brms_mmrm_transform_marginal)
 S3method(summary,brms_mmrm_archetype)
 S3method(summary,brms_mmrm_transform_marginal)
 S3method(transform_marginal_names_continuous,brms_mmrm_archetype)

R/brm_archetype.R

@@ -169,6 +169,7 @@ brm_archetype_attributes <- function(data) {
 
 #' @title Summarize an informative prior archetype.
 #' @export
+#' @keywords internal
 #' @description For an informative prior archetype, show
 #'   the transformation from model parameters to marginal means.
 #' @return Return a character vector with linear equations
@@ -181,6 +182,33 @@ brm_archetype_attributes <- function(data) {
 #'   to forgo verbose messages and non-invisibly return the equations.
 #' @param ... Not used, but required for S3 methods that inherit from
 #'   the base generic [summary()].
+#' @examples
+#' data <- brm_simulate_outline(
+#'   n_group = 2,
+#'   n_patient = 100,
+#'   n_time = 4,
+#'   rate_dropout = 0,
+#'   rate_lapse = 0
+#' ) |>
+#'   dplyr::mutate(response = rnorm(n = dplyr::n())) |>
+#'   brm_data_change() |>
+#'   brm_simulate_continuous(names = c("biomarker1", "biomarker2")) |>
+#'   brm_simulate_categorical(
+#'     names = c("status1", "status2"),
+#'     levels = c("present", "absent")
+#'   )
+#' dplyr::select(
+#'   data,
+#'   group,
+#'   time,
+#'   patient,
+#'   starts_with("biomarker"),
+#'   starts_with("status")
+#' )
+#' archetype <- brm_archetype_successive_cells(data)
+#' equations <- summary(archetype)
+#' print(equations)
+#' summary(archetype, message = FALSE)
 summary.brms_mmrm_archetype <- function(object, message = TRUE, ...) {
   formula <- brm_formula(object)
   transform <- brm_transform_marginal(

R/brm_transform_marginal.R

@@ -52,7 +52,6 @@
 #'   reconstruct the `brms` model parameter names. This argument should
 #'   only be modified for testing purposes.
 #' @examples
-#' if (identical(Sys.getenv("BRM_EXAMPLES", unset = ""), "true")) {
 #' set.seed(0L)
 #' data <- brm_data(
 #'   data = brm_simulate_simple()$data,
@@ -74,7 +73,6 @@
 #' summary(transform, message = FALSE)
 #' class(transform)
 #' print(transform)
-#' }
 brm_transform_marginal <- function(
   data,
   formula,
@@ -293,6 +291,7 @@ brm_transform_marginal_names_rows <- function(data, formula, grid) {
 
 #' @title Summarize marginal transform.
 #' @export
+#' @keywords internal
 #' @description Summarize a transformation from model parameters to
 #'   marginal means.
 #' @return Return a character vector with linear equations
@@ -305,6 +304,28 @@ brm_transform_marginal_names_rows <- function(data, formula, grid) {
 #'   to forgo verbose messages and non-invisibly return the equations.
 #' @param ... Not used, but required for S3 methods that inherit from
 #'   the base generic [summary()].
+#' @examples
+#' set.seed(0L)
+#' data <- brm_data(
+#'   data = brm_simulate_simple()$data,
+#'   outcome = "response",
+#'   group = "group",
+#'   time = "time",
+#'   patient = "patient",
+#'   reference_group = "group_1",
+#'   reference_time = "time_1"
+#' )
+#' formula <- brm_formula(
+#'   data = data,
+#'   baseline = FALSE,
+#'   baseline_time = FALSE
+#' )
+#' transform <- brm_transform_marginal(data = data, formula = formula)
+#' equations <- summary(transform)
+#' print(equations)
+#' summary(transform, message = FALSE)
+#' class(transform)
+#' print(transform)
 summary.brms_mmrm_transform_marginal <- function(
   object,
   message = TRUE,
@@ -328,6 +349,12 @@ summary.brms_mmrm_transform_marginal <- function(
   }
 }
 
+#' @export
+print.brms_mmrm_transform_marginal <- function(x, ...) {
+  class(x) <- c("matrix", "array")
+  NextMethod()
+}
+
 brm_transform_marginal_lines <- function(transform) {
   lines <- character(0L)
   marginals <- gsub(brm_sep(), ":", rownames(transform), fixed = TRUE)

tests/testthat/test-brm_transform_marginal.R

@@ -49,6 +49,7 @@ test_that("brm_transform_marginal(), response, non-subgroup", {
       expect_true(is.character(summary(transform, message = TRUE)))
     )
     expect_true(is.character(summary(transform, message = FALSE)))
+    tmp <- capture.output(print(transform))
     model_matrix <- brms::make_standata(
       formula = formula,
       data = dplyr::mutate(data, FEV1 = 0)