# vegdist

0th

Percentile

##### Good Dissimilarity Measures for Ecological Gradients

The function computes community dissimilarity indices which are known to have a good rank-order relation with gradient separation and are thus efficient in community ordination with multidimensional scaling.

Keywords
multivariate
##### Usage
vegdist(x, method="bray", diag=FALSE, upper=FALSE)
##### Arguments
x
Community data matrix
method
Dissimilarity index
diag
Compute diagonals.
upper
Return only the upper diagonal.
##### Details

The function knows the following dissimilarity indices: ll{ euclidean $d_{jk} = \sqrt{\sum_i (x_{ij}-x_{ik})^2}$ manhattan $d_{jk} = \sum_i |x_{ij} - x{ik}|$ gower $d_{jk} = \sum_i \frac{|x_{ij}-x_{ik}|}{\max_i-\min_i}$ canberra $d_{jk}=\frac{1}{N-Z} \sum_i \frac{|x_{ij}-x{ik}|}{x_{ij}+x_{ik}}$ bray $\frac{\sum_i |x_{ij}-x{ik}|}{\sum_i (x_{ij}+x_{ik})}$ kulczynski $d_{jk} = 1-0.5(\frac{\sum_i \min(x_{ij},x_{ik})}{\sum_i x_{ij}} + \frac{\sum_i \min(x_{ij},x_{ik})}{\sum_i x_{ik}} )$ } where $N-Z$ is the number of non-zero entries.

Infamous double zeros'' are removed in Canberra dissimilarity.

Euclidean and Manhattan dissimilarities are not good in gradient separation without proper standardization but are still included for comparison and special needs.

Some of indices become identical or rank-order similar after some standardizations.

##### Value

• Should be interchangeable with dist and return a distance object of the same type.

##### Note

The function is an alternative to dist adding some ecologically meaningful indices. Both methods should produce similar types of objects which can be interchanged in any method accepting either. Manhattan and Euclidean dissimilarities should be identical in both methods, and Canberra dissimilary may be similar.

##### References

Faith, D.P, Minchin, P.R. and Belbin, L. (1987) Compositional dissimilarity as a robust measure of ecological distance. Vegetatio 69, 57-68.

decostand, dist, rankindex, isoMDS
data(varespec)
vare.dist <- vegdist(varespec)