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Hamming distance

Source: R/beta_div.r
hamming.Rd

Measures the minimum number of substitutions required to change one string into the other.

Usage

hamming(counts, margin = 1L, pairs = NULL, cpus = n_cpus())

Arguments

counts

A numeric matrix of count data (samples \(\times\) features). Typically contains absolute abundances (integer counts), though proportions are also accepted.

margin

The margin containing samples. 1 if samples are rows, 2 if samples are columns. Ignored when counts is a special object class (e.g. phyloseq). Default: 1

pairs

Which combinations of samples should distances be calculated for? The default value (NULL) calculates all-vs-all. Provide a numeric or logical vector specifying positions in the distance matrix to calculate. See examples.

cpus

How many parallel processing threads should be used. The default, n_cpus(), will use all logical CPU cores.

Details

The Hamming distance is defined as: $$(A + B) - 2J$$

Where:

  • \(A\), \(B\) : Number of features in each sample.

  • \(J\) : Number of features in common (intersection).

Base R Equivalent:

x <- ex_counts[1,]
y <- ex_counts[2,]
sum(xor(x, y))

Input Types

The counts parameter is designed to accept a simple numeric matrix, but seamlessly supports objects from the following biological data packages:

  • phyloseq

  • rbiom

  • SummarizedExperiment

  • TreeSummarizedExperiment

For large datasets, standard matrix operations may be slow. See vignette('performance') for details on using optimized formats (e.g. sparse matrices) and parallel processing.

References

Hamming, R. W. (1950). Error detecting and error correcting codes. Bell System Technical Journal, 29(2), 147-160. doi:10.1002/j.1538-7305.1950.tb00463.x

See also

beta_div(), vignette('bdiv'), vignette('bdiv_guide')

Other Presence/Absence metrics: jaccard(), ochiai(), sorensen()

Examples

    hamming(ex_counts)
#>       Saliva Gums Nose
#> Gums       1          
#> Nose       2    1     
#> Stool      3    2    1