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 in the set \({-1, 1}^{R x n}\) 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.
Examples
if (FALSE) { # \dontrun{
set.seed(1)
W = matrix(sample(c(-1, 1), 6 * 8, replace = TRUE), nrow = 6)
res = greedy_orthogonalization_curation(W, Rmin = 3, verbose = FALSE)
res$avg_abs_rij_by_R
} # }
