ade4 (version 1.01)

dist.binary: Computation of Distance Matrices for Binary Data

Description

computes for binary data some distances matrices.

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

Value

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

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 $s_1 = \frac{a}{a+b+c}$} 2 = Sockal & Michener index (1958){S4 coefficient of Gower & Legendre $s_2 =\frac{a+d}{a+b+c+d}$} 3 = Sockal & Sneath(1963){S5 coefficient of Gower & Legendre $s_3 =\frac{a}{a+2(b+c)}$} 4 = Rogers & Tanimoto (1960){S6 coefficient of Gower & Legendre $s_4 =\frac{a+d}{(a+2(b+c)+d)}$} 5 = Czekanowski (1913) or Sorensen (1948){S7 coefficient of Gower & Legendre $s_5 =\frac{2*a}{2*a+b+c}$} 6 = S9 index of Gower & Legendre (1986){$s_6 =\frac{a-(b+c)+d}{a+b+c+d}$} 7 = Ochiai (1957){S12 coefficient of Gower & Legendre $s_7 =\frac{a}{\sqrt{(a+b)(a+c)}}$} 8 = Sockal & Sneath (1963){S13 coefficient of Gower & Legendre $s_8 =\frac{ad}{sqrt{(a+b)(a+c)(d+b)(d+c)}}$} 9 = Phi of Pearson{S14 coefficient of Gower & Legendre $s_9 =\frac{ad-bc}{\sqrt{(a+b)(a+c)(b+d)(d+c)}}$} 10 = S2 coefficient of Gower & Legendre{$s_10 = \frac{a}{a+b+c+d}$}

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), "")}