initGreedyExperimentalDesignObject {GreedyExperimentalDesign} | R Documentation |
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 |
nT |
The number of treatments to assign. Default is |
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 |
objective |
The objective function to use when searching design space. This is a string
with valid values " |
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 |
Kgram |
If the |
wait |
Should the |
start |
Should we start searching immediately (default is |
max_iters |
Should we impose a maximum number of greedy switches? The default is |
semigreedy |
Should we use a fully greedy approach or the quicker semi-greedy approach? The default is
|
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 |
num_cores |
The number of CPU cores you wish to use during the search. The default is |
seed |
The set to set for deterministic output. This should only be set if |
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)