vegdist(x, method="bray", diag=FALSE, upper=FALSE)"manhattan",
    "euclidean", "canberra", "bray", "kulczynski",
     "jaccard", "gower", "morisita", "horndist and
  return a distance object of the same type.veganeuclidean
    	$d_{jk} = \sqrt{\sum_i (x_{ij}-x_{ik})^2}$
    
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")Run the code above in your browser using DataLab