vegan (version 1.11-0)

vegdist: Dissimilarity Indices for Community Ecologists

Description

The function computes dissimilarity indices that are useful for or popular with community ecologists. All indices use quantitative data, although they would be named by the corresponding binary index, but you can calculate the binary index using an appropriate argument. If you do not find your favourite index here, you can see if it can be implemented using designdist. Gower, Bray--Curtis, Jaccard and Kulczynski indices are good in detecting underlying ecological gradients (Faith et al. 1987). Morisita, Horn--Morisita, Binomial and Chao indices should be able to handle different sample sizes (Wolda 1981, Krebs 1999, Anderson & Millar 2004), and Mountford (1962) and Raup-Crick indices for presence--absence data should be able to handle unknown (and variable) sample sizes.

Usage

vegdist(x, method="bray", binary=FALSE, diag=FALSE, upper=FALSE,
        na.rm = FALSE, ...)

Arguments

x

Community data matrix.

method

Dissimilarity index, partial match to "manhattan", "euclidean", "canberra", "bray", "kulczynski", "jaccard", "gower", "morisita", "horn

binary

Perform presence/absence standardization before analysis using decostand.

diag

Compute diagonals.

upper

Return only the upper diagonal.

na.rm

Pairwise deletion of missing observations when computing dissimilarities.

...

Other parameters. These are ignored, except in method ="gower" which accepts range.global parameter of decostand. .

Value

• Should provide a drop-in replacement for dist and return a distance object of the same type.

encoding

UTF-8

eqn

$S_j$

code

vegan

enc

• Sørensen
• Ružička

Details

Jaccard ("jaccard"), Mountford ("mountford"), Raup--Crick ("raup"), Binomial and Chao indices are discussed below. The other indices are defined as: ll{ euclidean $d_{jk} = \sqrt{\sum_i (x_{ij}-x_{ik})^2}$ manhattan $d_{jk} = \sum_i |x_{ij} - x_{ik}|$ gower $d_{jk} = (1/M) \sum_i \frac{|x_{ij}-x_{ik}|}{\max x_i-\min x_i}$ where $M$ is the number of columns (excluding missing values) 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 $n_i = x_{ij} + x_{ik}$

References

Anderson, M.J. and Millar, R.B. (2004). Spatial variation and effects of habitat on temperate reef fish assemblages in northeastern New Zealand. Journal of Experimental Marine Biology and Ecology 305, 191--221.

Chao, A., Chazdon, R. L., Colwell, R. K. and Shen, T. (2005). A new statistical approach for assessing similarity of species composition with incidence and abundance data. Ecology Letters 8, 148--159. Faith, D. P, Minchin, P. R. and Belbin, L. (1987). Compositional dissimilarity as a robust measure of ecological distance. Vegetatio 69, 57--68.

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.

See Also

Function designdist can be used for defining your own dissimilarity index. Alternative dissimilarity functions include dist in base R, daisy (package cluster), and dsvdis (package labdsv).

Examples

data(varespec)
vare.dist <- vegdist(varespec)
# Orlóci's Chord distance: range 0 .. sqrt(2)
vare.dist <- vegdist(decostand(varespec, "norm"), "euclidean")