Computation of Distance Matrices for Binary Data
computes for binary data some distance matrice.
dist.binary(df, method = NULL, diag = FALSE, upper = FALSE)
df |
a matrix or a data frame with positive or null numeric values. Used with |
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’ |
Let be the contingency table of binary data such as n11 = a, n10 = b, n01 = c and n00 = d. All these distances are of type d = sqrt(1 - s) with s a similarity coefficient.
S3 coefficient of Gower & Legendre s1 = a / (a+b+c)
S4 coefficient of Gower & Legendre s2 = (a+d) / (a+b+c+d)
S5 coefficient of Gower & Legendre s3 = a / (a + 2(b + c))
S6 coefficient of Gower & Legendre s4 = (a + d) / (a + 2(b + c) +d)
S7 coefficient of Gower & Legendre s5 = 2a / (2a + b + c)
S9 index of Gower & Legendre (1986) s6 = (a - (b + c) + d) / (a + b + c + d)
S12 coefficient of Gower & Legendre s7 = a / sqrt((a + b)(a + c))
S13 coefficient of Gower & Legendre s8 = ad / sqrt((a + b)(a + c)(d + b)(d + c))
S14 coefficient of Gower & Legendre s9 = (ad - bc) / sqrt((a + b)(a + c)(d + b)(d + c))
s10 = a / (a + b + c + d)
returns a distance matrix of class dist between the rows of the data frame
Daniel Chessel
Stéphane Dray stephane.dray@univ-lyon1.fr
Gower, J.C. and Legendre, P. (1986) Metric and Euclidean properties of dissimilarity coefficients. Journal of Classification, 3, 5–48.
data(aviurba)
for (i in 1:10) {
d <- dist.binary(aviurba$fau, method = i)
cat(attr(d, "method"), is.euclid(d), "\n")}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.