Compute Binary Matching Strcuture

This method creates an object of type binary_match_structure and will compute pairs. You can then use the functions initBinaryMatchExperimentalDesignSearchObject and resultsBinaryMatchSearch to create randomized allocation vectors. For one column in X, we just sort to find the pairs trivially.

Usage

computeBinaryMatchStructure(
  X,
  mahal_match = FALSE,
  compute_dist_matrix = NULL,
  D = NULL,
  symmetry_tol = 1e-12,
  use_safe_inverse = FALSE
)

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.

mahal_match

Match using Mahalanobis distance. Default is 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++.

D

A distance matrix precomputed. The default is NULL indicating the distance matrix should be computed.

symmetry_tol

Tolerance for symmetry check on D. Default is 1e-12.

use_safe_inverse

Should a regularized inverse be used for the Mahalanobis objective? Default is FALSE.

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)
bms = computeBinaryMatchStructure(X)
bms$indicies_pairs
} # }