# NOT RUN {
n <- 500
population <- declare_population(N = 1000)
sampling <- declare_sampling(n = n)
design <- population + sampling
# returns a single, modified design
modified_design <- redesign(design, n = 200)
# returns a list of six modified designs
design_vary_N <- redesign(design, n = seq(400, 900, 100))
# 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)
assignment <- declare_assignment(prob_each = prob_each)
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_population(N = 1, A = X) + NULL
design2 <- declare_population(N = 1, A = f(2, X)) + NULL
design3 <- declare_population(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))
# }
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