binaryDistance function defines various similarity or distance
measures between binary vectors, which represent the first step in the
algorithm underlying the
An object of class
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:
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
binary distance is also included.
Returns an object of class
dist corresponding to the distance
Although the distance metrics provided in the
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 <firstname.lastname@example.org>, 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
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