vegan (version 2.0-10)

rankindex: 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. Alternatively, it can be a (named) list of functions returning objects of class 'dist'.

stepacross

Use stepacross to find a shorter path dissimilarity. The dissimilarities for site pairs with no shared species are set NA using no.shared<

method

Correlation method used.

...

Other parameters to stepacross.

Value

• Returns a named vector of rank correlations.

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.

The indices argument can accept any dissimilarity indices besides the ones calculated by the vegdist function. For this, the argument value should be a (possibly named) list of functions. Each function must return a valid 'dist' object with dissimilarities, similarities are not accepted and should be converted into dissimilarities beforehand.

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, monoMDS, 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))
## Using non vegdist indices as functions
funs <- list(Manhattan=function(x) dist(x, "manhattan"),
    Gower=function(x) cluster:::daisy(x, "gower"),
    Ochiai=function(x) designdist(x, "1-J/sqrt(A*B)"))
rankindex(scale(varechem), varespec, funs)