vegdist(x, method="bray", diag=FALSE, upper=FALSE)
"manhattan"
,
"euclidean"
, "canberra"
, "bray"
, "kulczynski"
,
"jaccard"
, "gower"
, "morisita"
, "horn
dist
and
return a distance object of the same type.vegan
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
$d_{jk}=\frac{1}{N-Z} \sum_i
\frac{|x_{ij}-x_{ik}|}{x_{ij}+x_{ik}}$
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 $\lambda_j = \sum_i
x_{ij}^2/(\sum_i x_{ij})^2$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
.data(varespec)
vare.dist <- vegdist(varespec)
# Orl�ci's Chord distance: range 0 .. sqrt(2)
vare.dist <- vegdist(decostand(varespec, "norm"), "euclidean")
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