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.
Examples
if (FALSE) { # \dontrun{
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)
} # }
