greedy_orthogonalization_curation {GreedyExperimentalDesign}

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_curation(W, Rmin = 2, verbose = FALSE)

Arguments

W

A matrix in $-1, 1^R x n$ which have R allocation vectors for an experiment of sample size n.

Rmin

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