R/vectorHammingDist.R

In waldronlab/doppelgangR: Identify likely duplicate samples from genomic or meta-data

#' Calculate Hamming Distance between two vectors, using pairwise complete
#' observations.
#'
#' Simple function to count the fraction of different elements (in the same
#' position) between two vectors of the same length, after removing elements
#' from both vectors corresponding to positions that are NA in either vector.
#'
#'
#' @param x a matrix
#' @param y a matrix with the same number of columns as x
#' @param k row in x to test for differences
#' @param l row in y to test for differences
#' @return Returns a numeric value, the Hamming Distance (the number of
#' non-equal values between x and y).
#' @author Levi Waldron, Markus Riester, Marcel Ramos
#' @examples
#'
#' (mat <- matrix(c(paste0("A", 1:5), paste0("A", 5:1)),
#'     nrow = 2, byrow = TRUE))
#'
#' stopifnot(vectorHammingDist(mat, mat, 1, 2) == 0.8)
#' stopifnot(vectorHammingDist(mat, mat, 1, 1) == 0)
#'
#' mat[1, 1] <- NA
#'
#' stopifnot(vectorHammingDist(mat, mat, 1, 2) == 0.75)
#' stopifnot(vectorHammingDist(mat, mat, 1, 1) == 0)
#'
#' mat[1, 3] <- NA
#'
#' stopifnot(vectorHammingDist(mat, mat, 1, 2) == 1)
#'
#' @export vectorHammingDist
vectorHammingDist <- function(x, y, k, l) {
    z <- as.vector(x[k, ] != y[l, ])
    sum(z, na.rm = TRUE) / length(na.omit(z))
}