vegdist(x, method="bray", binary=FALSE, diag=FALSE, upper=FALSE,
na.rm = FALSE, ...)"manhattan",
"euclidean", "canberra", "bray", "kulczynski",
"jaccard", "gower", "morisita", "horndecostand.method ="gower" which accepts range.global parameter of
decostand. .dist and
return a distance object of the same type.vegan"jaccard"), Mountford ("mountford"),
Raup--Crick ("raup") and Binomial indices are discussed below.
The other indices are defined as:
euclidean
$d_{jk} = \sqrt{\sum_i (x_{ij}-x_{ik})^2}$
manhattan
$d_{jk} = \sum_i |x_{ij} - x_{ik}|$
gower
$d_{jk} = \sum_i \frac{|x_{ij}-x_{ik}|}{\max x_i-\min x_i}$
canberra
$d_{jk}=\frac{1}{NZ} \sum_i
\frac{|x_{ij}-x_{ik}|}{x_{ij}+x_{ik}}$
where $NZ$ is the number of non-zero entries.
bray
$d_{jk} = \frac{\sum_i |x_{ij}-x_{ik}|}{\sum_i (x_{ij}+x_{ik})}$
kulczynski
$d_{jk} = 1-0.5(\frac{\sum_i \min(x_{ij},x_{ik})}{\sum_i x_{ij}} +
\frac{\sum_i \min(x_{ij},x_{ik})}{\sum_i x_{ik}} )$
morisita
$d_{jk} = \frac{2 \sum_i x_{ij} x_{ik}}{(\lambda_j +
\lambda_k) \sum_i x_{ij} \sum_i
x_{ik}}$ }
where $\lambda_j = \frac{\sum_i x_{ij} (x_{ij} - 1)}{\sum_i
x_{ij} \sum_i (x_{ij} - 1)}$
horn
Like morisita, but $\lambda_j = \sum_i
x_{ij}^2/(\sum_i x_{ij})^2$
binomial
$d_{jk} = \sum_i [x_{ij} \log (\frac{x_{ij}}{n_i}) + x_{ik} \log
(\frac{x_{ik}}{n_i}) - n_i \log(\frac{1}{2})]/n_i$
where $n_i = x_{ij} + x_{ik}$Krebs, C. J. (1999). Ecological Methodology. Addison Wesley Longman.
Legendre, P, & Legendre, L. (1998) Numerical Ecology. 2nd English Edition. Elsevier.
Mountford, M. D. (1962). An index of similarity and its application to classification problems. In: P.W.Murphy (ed.), Progress in Soil Zoology, 43--50. Butterworths.
Wolda, H. (1981). Similarity indices, sample size and diversity. Oecologia 50, 296--302.
decostand, dist,
rankindex, isoMDS,
stepacross, daisy,
dsvdis.data(varespec)
vare.dist <- vegdist(varespec)
# Orlóci's Chord distance: range 0 .. sqrt(2)
vare.dist <- vegdist(decostand(varespec, "norm"), "euclidean")Run the code above in your browser using DataLab