greedy_orthogonalization_curation2: Curate More Orthogonal Vectors Greedily

In GreedyExperimentalDesign: Greedy Experimental Design Construction

Curate More Orthogonal Vectors Greedily

Description

This function takes a set of allocation vectors and pares them down one-by-one by eliminating the vector that can result in the largest reduction in Avg[ |r_ij| ]. It is recommended to begin with a set of unmirrored vectors for speed. Then add the mirrors later for whichever subset you wish.

Usage

greedy_orthogonalization_curation2(W, R0 = 100, verbose = FALSE)

Arguments

W	A matrix in {-1, 1}^{R x n} which have R allocation vectors for an experiment of sample size n.
R0	The minimum number of vectors to consider in a design. The default is the true bottom, two.
verbose	Default is FALSE but if not, it will print out a message for each iteration.

Value

A list with two elements: (1) avg_abs_rij_by_R which is a data frame with R - Rmin + 1 rows and columns R and average absolute r_ij and (2) Wsorted which provides the collection of vectors in sorted by best average absolute r_ij in row order from best to worst.

Author(s)

Adam Kapelner

Examples

## Not run: 
set.seed(1)
W = matrix(sample(c(-1, 1), 6 * 8, replace = TRUE), nrow = 6)
W2 = greedy_orthogonalization_curation2(W, R0 = 4, verbose = FALSE)
dim(W2)

## End(Not run)

GreedyExperimentalDesign documentation built on Jan. 9, 2026, 5:07 p.m.