redesign
quickly generates a design from an existing one by resetting symbols used in design handler parameters in a step's environment (Advanced).
redesign(design, ..., expand = TRUE)
design | An object of class design. |
---|---|
... | Arguments to redesign e.g., |
expand | If TRUE, redesign using the crossproduct of |
A design, or, in the case of multiple values being passed onto ...
, a `by`-list of designs.
Warning: 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 redesign
functionality is experimental and may be changed in future versions.
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)#> ID A #> 1 1 3#> ID A #> 1 1 6#> ID A #> 1 1 6#> ID A #> 1 1 0#> ID A #> 1 1 0#> ID A #> 1 1 6