This method creates an object of type rerandomization_experimental_design and will immediately initiate
a search through $1_T$ space for forced-balance designs. For debugging, you can use set the seed
parameter and num_cores = 1 to be assured of deterministic output.
initRerandomizationExperimentalDesignObject(
X = NULL,
obj_val_cutoff_to_include,
max_designs = 1000,
objective = "mahal_dist",
Kgram = NULL,
wait = FALSE,
start = TRUE,
num_cores = 1,
seed = NULL
)An object of type rerandomization_experimental_design_search which can be further operated upon.
The design matrix with $n$ rows (one for each subject) and $p$ columns (one for each measurement on the subject). This is the design matrix you wish to search for a more optimal design.
Only allocation vectors with objective values lower than this threshold will be returned.
If the cutoff is infinity, you are doing BCRD and you should use the complete_randomization_with_forced_balanced
function instead.
The maximum number of designs to be returned. Default is 10,000. Make this large
so you can search however long you wish as the search can be stopped at any time by
using the stopSearch method
The objective function to use when searching design space. This is a string
with valid values "mahal_dist" (the default), "abs_sum_diff" or "kernel".
If the objective = kernel, this argument is required to be an n x n matrix whose
entries are the evaluation of the kernel function between subject i and subject j. Default is NULL.
Should the R terminal hang until all max_designs vectors are found? The
default is FALSE.
Should we start searching immediately (default is TRUE).
The number of CPU cores you wish to use during the search. The default is 1.
The set to set for deterministic output. This should only be set if num_cores = 1 otherwise
the output will not be deterministic. Default is NULL for no seed set.
Adam Kapelner