rankindex {vegan} | R Documentation

## Compares Dissimilarity Indices for Gradient Detection

### Description

Rank correlations between dissimilarity indices and gradient separation.

### Usage

    rankindex(grad, veg, indices = c("euc", "man", "gow", "bra", "kul"),
              stepacross = FALSE, method = "spearman", ...)

### Arguments

`grad` | The gradient variable or matrix.
`veg` | The community data matrix.
`indices` | Dissimilarity indices compared, partial matches to alternatives in `vegdist`.
`stepacross` | Use `stepacross` to find a shorter path dissimilarity. The dissimilarities for site pairs with no shared species are set `NA` using `no.shared` so that indices with no fixed upper limit can also be analysed.
`method` | Correlation method used.
`...` | Other parameters to `stepacross`.

### Details

A good dissimilarity index for multidimensional scaling should have a high rank-order similarity with gradient separation. The function compares most indices in `vegdist` against gradient separation using rank correlation coefficients in `cor.test`. The gradient separation between each point is assessed as Euclidean distance for continuous variables, and as Gower metric for mixed data using function `daisy` when `grad` has factors.

### Value

Returns a named vector of rank correlations.

### Note

There are several problems in using rank correlation coefficients. Typically there are very many ties when n(n-1)/2 gradient separation values are derived from just n observations. Due to floating point arithmetics, many tied values differ by machine epsilon and are arbitrarily ranked differently by `rank` used in `cor.test`. Two indices which are identical with certain transformation or standardization may differ slightly (magnitude 10^{-15}) and this may lead into third or fourth decimal instability in rank correlations. Small differences in rank correlations should not be taken too seriously. Probably this method should be replaced with a sounder method, but I do not yet know which... You may experiment with `mantel`, `anosim` or even `protest`.

Earlier version of this function used `method = "kendall"`, but that is far too slow in large data sets.

### Author(s)

Jari Oksanen

### References

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

### See Also

`vegdist`, `stepacross`, `no.shared`, `isoMDS`, `cor`, `Machine`, and for alternatives `anosim`, `mantel` and `protest`.

### Examples

    data(varespec)
    data(varechem)
    ## The next scales all environmental variables to unit variance.
    ## Some would use PCA transformation.
    rankindex(scale(varechem), varespec)
    rankindex(scale(varechem), wisconsin(varespec))

[Package vegan version 1.8-6 Index]