c0cat() preserves the multiset of categories within each column but
randomizes their positions across rows, leaving row margins free.
This is the categorical analog to vegan's c0 algorithm. It is a
non-sequential method.
Usage
c0cat(x, n_iter = 1L, output = c("category", "index"), seed = NULL)Arguments
- x
A matrix of categorical data, encoded as integers. Values should represent category or stratum membership for each cell.
- n_iter
Number of iterations. Default is 1000. Larger values yield more thorough mixing. Ignored for non-sequential methods. Minimum burn-in times can be estimated with
suggest_n_iter().- output
Character indicating type of result to return:
"category"(default) returns randomized matrix"index"returns an index matrix describing where original entries (a.k.a. "tokens") moved. Useful mainly for testing, and for applications likequantize()that care about token tracking in addition to generic integer categories.
- seed
Integer used to seed random number generator, for reproducibility.
Value
A matrix of the same dimensions as x, either randomized
categorical values (when output = "category") or an integer index
matrix describing the permutation of entries (when output = "index").
Examples
set.seed(123)
x <- matrix(sample(1:4, 100, replace = TRUE), nrow = 10)
# Randomize within columns (column margins fixed, row margins free)
x_rand <- c0cat(x)
# Verify columns are preserved but rows are not
all.equal(sort(x[, 1]), sort(x_rand[, 1]))
#> [1] TRUE
any(sort(x[1, ]) != sort(x_rand[1, ]))
#> [1] TRUE