dist.binary {ade4} | R Documentation

## Computation of Distance Matrices for Binary Data

### Description

computes for binary data some distance matrice.

### Usage

    dist.binary(df, method = NULL, diag = FALSE, upper = FALSE)

### Arguments

`df` | a data frame with positive or zero values. Used with `as.matrix(1 * (df > 0))`
`method` | an integer between 1 and 10 . If NULL the choice is made with a console message. See details
`diag` | a logical value indicating whether the diagonal of the distance matrix should be printed by `print.dist'
`upper` | a logical value indicating whether the upper triangle of the distance matrix should be printed by `print.dist'

### Details

All these distances are of type d = sqrt(1 - s) with s a similarity coefficient.

1 = Jaccard index (1901)
  S3 coefficient of Gower & Legendre s1 = a / (a+b+c)
2 = Sockal & Michener index (1958)
  S4 coefficient of Gower & Legendre s2 = (a+d) / (a+b+c+d)
3 = Sockal & Sneath(1963)
  S5 coefficient of Gower & Legendre s3 = a / (a + 2(b + c))
4 = Rogers & Tanimoto (1960)
  S6 coefficient of Gower & Legendre s4 = (a + d) / (a + 2(b + c) +d)
5 = Czekanowski (1913) or Sorensen (1948)
  S7 coefficient of Gower & Legendre s5 = 2a / (2a + b + c)
6 = S9 index of Gower & Legendre (1986)
  s6 = (a - (b + c) + d) / (a + b + c + d)
7 = Ochiai (1957)
  S12 coefficient of Gower & Legendre s7 = a / sqrt((a + b)(a + c))
8 = Sockal & Sneath (1963)
  S13 coefficient of Gower & Legendre s8 = ad / sqrt((a + b)(a + c)(d + b)(d + c))
9 = Phi of Pearson
  S14 coefficient of Gower & Legendre s9 = (ad - bc) / sqrt((a + b)(a + c)(d + b)(d + c))
10 = S2 coefficient of Gower & Legendre
  s10 = a / (a + b + c + d)

### Value

returns a distance matrix of class `dist` between the rows of the data frame

### Author(s)

Daniel Chessel chessel@biomserv.univ-lyon1.fr

### References

Gower, J.C. and Legendre, P. (1986) Metric and Euclidean properties of dissimilarity coefficients. Journal of Classification, 3, 5–48.

### Examples

    data(aviurba)
    for (i in 1:10) {
        d <- dist.binary(aviurba$fau, method = i)
        cat(attr(d, "method"), is.euclid(d), "\n")}

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