distance: Calculates the Similarity or Dissimilarity Between Two Fingerprints

Description

A number of distance metrics can be calculated for binary fingerprints. Some of these are actually similarity metrics and thus represent the reverse of a distance metric.

The following are distance (dissimilarity) metrics

Hamming
Mean Hamming
Soergel
Pattern Difference
Variance
Size
Shape

The following metrics are similarity metrics and so the distance can be obtained by subtracting the value fom 1.0

Tanimoto
Dice
Modified Tanimoto
Simple
Jaccard
Russel-Rao
Rodgers Tanimoto
Cosine
Achiai
Carbo
Baroniurbanibuser
Kulczynski2

Finally the method also provides a set of composite and asymmetric distance metrics

Hamann
Yule
Pearson
Dispersion
McConnaughey
Stiles
Simpson
Petke

The default metric is the Tanimoto coefficient.

Usage

distance(fp1, fp2, method)

Arguments

fp1

An object of class fingerprint

fp2

An object of class fingerprint

method

The type of distance metric desired. Partial matching is supported and the deault is tanimoto. Alternative values are

euclidean
hamming
meanHamming
soergel

`Value`

Numeric value representing the distance in the specified metric between the
  supplied fingerprint objects

`References`

Fligner, M.A.; Verducci, J.S.; Blower, P.E.;
  A Modification of the Jaccard-Tanimoto Similarity Index for
  Diverse Selection of Chemical Compounds Using Binary Strings,
  Technometrics, 2002, 44(2), 110-119
  Monve, V.; Introduction to Similarity Searching in
  Chemistry, MATCH - Comm. Math. Comp. Chem., 2004, 51, 7-38

`Examples`

Run this code# make a 2 fingerprint vectors
fp1 <- new("fingerprint", nbit=6, bits=c(1,2,5,6))
fp2 <- new("fingerprint", nbit=6, bits=c(1,2,5,6))

# calculate the tanimoto coefficient
distance(fp1,fp2) # should be 1

# Invert the second fingerprint
fp3 <- !fp2

distance(fp1,fp3) # should be 0
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