Description Usage Arguments Details Value Note Author(s) References See Also Examples

The `binaryDistance`

function defines various similarity or distance
measures between binary vectors, which represent the first step in the
algorithm underlying the `Mercator`

visualizations.

1 | ```
binaryDistance(X, metric)
``` |

`X` |
An object of class |

`metric` |
An object of class |

Similarity or difference between binary vectors can be calculated using a variety of distance measures. In the main reference (below), Choi and colleagues reviewed 76 different measures of similarity of distance between binary vectors. They also produced a hierarchical clustering of these measures, based on the correlation between their distance values on multiple simulated data sets. For metrics that are highly similar, we chose a single representative.

Cluster 1, represented by the `jaccard`

distance, contains Dice & Sorenson, Ochiai,
Kulcyznski, Bray & Curtis, Baroni-Urbani & Buser, and Jaccard.

Cluster 2, represented by the `sokalMichener`

distance, contains Sokal & Sneath,
Gilbert & Wells, Gower & Legendre, Pearson & Heron, Hamming, and Sokal & Michener.
Also within this cluster are 4 distances represented independently within this function:
`hamming`

, `manhattan`

, `canberra`

, and `euclidean`

distances

Cluster 3, represented by the `russellRao`

distance, contains Driver & Kroeber,
Forbes, Fossum, and Russell & Rao.

The remaining metrics are more isolated, without strong clustering. We considered a few
examples, including the Pearson distance (`pearson`

) and the Goodman & Kruskal distance
(`goodmanKruskal`

). The `binary`

distance is also included.

Returns an object of class `dist`

corresponding to the distance
`metric`

provided.

Although the distance metrics provided in the `binaryDistance`

function
are explicitly offered for use on matrices of binary vectors, some metrics may
return useful distances when applied to non-binary matrices.

Kevin R. Coombes <krc@silicovore.com>, Caitlin E. Coombes

Choi SS, Cha SH, Tappert CC, A Survey of Binary Similarity and Distance Measures. Systemics, Cybernetics, and Informatics. 2010; 8(1):43-48.

This set includes all of the metrics from the `dist`

function.

1 2 | ```
my.matrix <- matrix(rbinom(50*100, 1, 0.15), ncol=50)
my.dist <- binaryDistance(my.matrix, "jaccard")
``` |

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