initGreedyExperimentalDesignObject {GreedyExperimentalDesign}

Begin A Greedy Pair Switching Search

Description

This method creates an object of type greedy_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.

Usage

initGreedyExperimentalDesignObject(
  X = NULL,
  nT = NULL,
  max_designs = 10000,
  objective = "mahal_dist",
  indicies_pairs = NULL,
  Kgram = NULL,
  wait = FALSE,
  start = TRUE,
  max_iters = Inf,
  semigreedy = FALSE,
  diagnostics = FALSE,
  num_cores = 1,
  seed = NULL
)

Arguments

X

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. This parameter must be specified unless you choose objective type "kernel" in which case, the Kgram parameter must be specified.

nT

The number of treatments to assign. Default is NULL which is for forced balance allocation i.e. nT = nC = n / 2 where n is the number of rows in X (or Kgram if X is unspecified).

max_designs

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

objective

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".

indicies_pairs

A matrix of size $n/2$ times 2 whose rows are indicies pairs. The values of the entire matrix must enumerate all indicies $1, ..., n$. The default is NULL meaning to use all possible pairs.

Kgram

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.

wait

Should the R terminal hang until all max_designs vectors are found? The deafult is FALSE.

start

Should we start searching immediately (default is TRUE).

max_iters

Should we impose a maximum number of greedy switches? The default is Inf which a flag for “no limit.”

semigreedy

Should we use a fully greedy approach or the quicker semi-greedy approach? The default is FALSE corresponding to the fully greedy approach.

diagnostics

Returns diagnostic information about the iterations including (a) the initial starting vectors, (b) the switches at every iteration and (c) information about the objective function at every iteration (default is FALSE to decrease the algorithm's run time).

num_cores

The number of CPU cores you wish to use during the search. The default is 1.

seed

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.

Value

An object of type greedy_experimental_design_search which can be further operated upon

Author(s)

Adam Kapelner

Examples

 ## Not run: 
	library(MASS)
	data(Boston)
 #pretend the Boston data was an experiment setting 
	#first pull out the covariates
 X = Boston[, 1 : 13] 
 #begin the greedy design search
	ged = initGreedyExperimentalDesignObject(X, 
		max_designs = 1000, num_cores = 3, objective = "abs_sum_diff")
	#wait
	ged
	
## End(Not run)