Begin a Search for Binary Matching Followed by Greedy Switch Designs

This method creates an object of type binary_then_greedy_experimental_design and will find optimal matched pairs which are then greedily switched in order to further minimize a balance metric. You can then use the function resultsBinaryMatchThenGreedySearch to obtain the randomized allocation vectors. For one column in X, the matching just sorts the values to find the pairs trivially.

Usage

initBinaryMatchFollowedByGreedyExperimentalDesignSearchObject(
  X,
  diff_method = FALSE,
  compute_dist_matrix = NULL,
  verbose = TRUE,
  ...
)

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.

diff_method

Once the subjects (i.e. row vectors) are paired, do we create a set of $n$/2 difference vectors and feed that into greedy? If TRUE, this technically breaks the objective function, but it is shown to have better performance. The default is thus FALSE.

compute_dist_matrix

The function that computes the distance matrix between every two observations in X, its only argument. The default is NULL signifying euclidean squared distance optimized in C++.

verbose

Should the algorithm emit progress output? Default is TRUE.

...

Arguments passed to initGreedyExperimentalDesignObject. It is recommended to set max_designs otherwise it will default to 10,000.

Value

An object of type binary_experimental_design which can be further operated upon.

Author

Adam Kapelner

Examples

if (FALSE) { # \dontrun{
set.seed(1)
X = matrix(rnorm(16), nrow = 8)
obj = initBinaryMatchFollowedByGreedyExperimentalDesignSearchObject(
  X,
  max_designs = 4,
  num_cores = 1,
  objective = "abs_sum_diff",
  start = TRUE,
  wait = TRUE,
  verbose = FALSE
)
obj
} # }