# hammingdists: Pairwise Hamming distances between matrix rows. In kevinstadler/cultevo: Tools, Measures and Statistical Tests for Cultural Evolution

## Description

Returns a distance matrix giving all pairwise Hamming distances between the rows of its argument `meanings`, which can be a matrix, data frame or vector. Vectors are treated as matrices with a single column, so the distances in its return value can only be 0 or 1.

## Usage

 `1` ```hammingdists(meanings) ```

## Arguments

 `meanings` a matrix with the different dimensions encoded along columns, and all combinations of meanings specified along rows. The data type of the cells does not matter since distance is simply based on equality (with the exception of `NA` values, see below.

## Details

This function behaves differently from calling `dist(meanings, method="manhattan")` in how `NA` values are treated: specifying a meaning component as `NA` allows you to ignore that dimension for the given row/meaning combinations, (instead of counting a difference between `NA` and another value as a distance of 1).

## Value

A distance matrix of type `dist` with `n*(n-1)/2` rows/columns, where n is the number of rows in `meanings`.

`dist`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```# a 2x2 design using strings print(strings <- matrix(c("a1", "b1", "a1", "b2", "a2", "b1", "a2", "b2"), ncol=2, byrow=TRUE)) hammingdists(strings) # a 2x3 design using integers print(integers <- matrix(c(0, 0, 0, 1, 0, 2, 1, 0, 1, 1, 1, 2), ncol=2, byrow=TRUE)) hammingdists(integers) # a 3x2 design using factors (ncol is always the number of dimensions) print(factors <- data.frame(colour=c("red", "red", "green", "blue"), animal=c("dog", "cat", "dog", "cat"))) hammingdists(factors) # if some meaning dimension is not relevant for some combinations of # meanings (e.g. optional arguments), specifying them as NA in the matrix # will make them not be counted towards the hamming distance! in this # example the value of the second dimension does not matter (and does not # count towards the distance) when the the first dimension has value '1' print(ignoredimension <- matrix(c(0, 0, 0, 1, 1, NA), ncol=2, byrow=TRUE)) hammingdists(ignoredimension) # trivial case of a vector: first and last two elements are identical, # otherwise a difference of one hammingdists(c(0, 0, 1, 1)) ```

kevinstadler/cultevo documentation built on April 25, 2018, 9:05 a.m.