R/brm_simulate_continuous.R

In brms.mmrm: Bayesian MMRMs using 'brms'

#' @title Append simulated continuous covariates
#' @export
#' @family simulation
#' @description Simulate and append non-time-varying continuous
#'   covariates to an existing [brm_data()] dataset.
#' @details Each covariate is a new column of the dataset with one independent
#'   random univariate normal draw for each patient.
#'   All covariates simulated this way are
#'   independent of everything else in the data, including other covariates
#'   (to the extent that the random number generators in R work as intended).
#' @return A classed `tibble`, like from [brm_data()] or
#'   [brm_simulate_outline()], but with new numeric covariate columns
#'   and with the names of the new covariates appended to the
#'   `brm_covariates` attribute.
#' @param data Classed `tibble` as from [brm_data()]
#'   or [brm_simulate_outline()].
#' @param names Character vector with the names of the new covariates
#'   to simulate and append. Names must all be unique and
#'   must not already be column names of `data`.
#' @param mean Numeric of length 1,
#'   mean of the normal distribution for simulating each covariate.
#' @param sd Positive numeric of length 1,
#'   standard deviation of the normal distribution
#'   for simulating each covariate.
#' @examples
#' data <- brm_simulate_outline()
#' brm_simulate_continuous(
#'   data = data,
#'   names = c("age", "biomarker")
#' )
#' brm_simulate_continuous(
#'   data = data,
#'   names = c("biomarker1", "biomarker2"),
#'   mean = 1000,
#'   sd = 100
#' )
brm_simulate_continuous <- function(
  data,
  names,
  mean = 0,
  sd = 1
) {
  brm_data_validate(data)
  assert_chr_vec(names, message = "names must be a valid character vector")
  assert_pos(length(names), message = "names must not be empty")
  assert_machine_names(names)
  assert(!anyDuplicated(names), message = "names must all be unique")
  assert(
    !any(names %in% colnames(data)),
    message = paste(
      "columns already in data:",
      paste(intersect(names, colnames(data)), collapse = ", ")
    )
  )
  assert_num(mean, message = "mean must be a valid number")
  assert_pos(sd, message = "sd must be a valid positive number")
  patient <- attr(data, "brm_patient")
  data_patient <- tibble::tibble(name = unique(data[[patient]]))
  colnames(data_patient) <- patient
  n <- nrow(data_patient)
  for (name in names) {
    data_patient[[name]] <- stats::rnorm(n = n, mean = mean, sd = sd)
  }
  data <- dplyr::left_join(x = data, y = data_patient, by = patient)
  attr(data, "brm_covariates") <- union(
    x = attr(data, "brm_covariates"),
    y = names
  )
  brm_data_validate(data)
  data
}