#' Redesign
#'
#' \code{redesign} quickly generates a design from an existing one by resetting symbols used in design handler parameters in a step's environment (Advanced).
#'
#' Warning: \code{redesign} will edit any symbol in your design, but if the symbol you attempt to change does not exist in a step's environment no changes will be made and no error or warning will be issued.
#'
#' Please note that \code{redesign} functionality is experimental and may be changed in future versions.
#'
#' @param design An object of class design.
#' @param ... Arguments to redesign e.g., \code{n = 100.} If redesigning multiple arguments, they must be specified as a named list.
#' @param expand If TRUE, redesign using the crossproduct of \code{...}, otherwise recycle them.
#' @return A design, or, in the case of multiple values being passed onto \code{...}, a `by`-list of designs.
#' @examples
#'
#' # Two-arm randomized experiment
#' n <- 500
#'
#' design <-
#' declare_model(
#' N = 1000,
#' gender = rbinom(N, 1, 0.5),
#' X = rep(c(0, 1), each = N / 2),
#' U = rnorm(N, sd = 0.25),
#' potential_outcomes(Y ~ 0.2 * Z + X + U)
#' ) +
#' declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
#' declare_sampling(S = complete_rs(N = N, n = n)) +
#' declare_assignment(Z = complete_ra(N = N, m = n/2)) +
#' declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
#' declare_estimator(Y ~ Z, inquiry = "ATE")
#'
#' # Use redesign to return a single modified design
#' modified_design <- redesign(design, n = 200)
#'
#' # Use redesign to return a series of modified designs
#' ## Sample size is varied while the rest of the design remains
#' ## constant
#' design_vary_N <- redesign(design, n = c(100, 500, 900))
#'
#' \dontrun{
#' # redesign can be used in conjunction with diagnose_designs
#' # to optimize the design for specific diagnosands
#' diagnose_designs(design_vary_N)
#' }
#'
#' # When redesigning with arguments that are vectors,
#' # use list() in redesign, with each list item
#' # representing a design you wish to create
#'
#' prob_each <- c(.1, .5, .4)
#'
#' population <- declare_model(N = 1000)
#' assignment <- declare_assignment(
#' Z = complete_ra(prob_each = prob_each),
#' legacy = FALSE)
#'
#' design <- population + assignment
#'
#' ## returns two designs
#'
#' designs_vary_prob_each <- redesign(
#' design,
#' prob_each = list(c(.2, .5, .3), c(0, .5, .5)))
#'
#' # To illustrate what does and does not get edited by redesign,
#' # consider the following three designs. In the first two, argument
#' # X is called from the step's environment; in the third it is not.
#' # Using redesign will alter the role of X in the first two designs
#' # but not the third one.
#'
#' X <- 3
#' f <- function(b, X) b*X
#' g <- function(b) b*X
#'
#' design1 <- declare_model(N = 1, A = X) + NULL
#' design2 <- declare_model(N = 1, A = f(2, X)) + NULL
#' design3 <- declare_model(N = 1, A = g(2)) + NULL
#'
#' draw_data(design1)
#' draw_data(design2)
#' draw_data(design3)
#'
#' draw_data(redesign(design1, X=0))
#' draw_data(redesign(design2, X=0))
#' draw_data(redesign(design3, X=0))
#'
#' @export
redesign <- function(design, ..., expand = TRUE) {
check_design_class_single(design)
f <- function(...) {
clone_design_edit(design, ...)
}
design <- expand_design(f, ..., expand = expand)
structure(design, code = NULL)
}
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