rankindex {vegan} | R Documentation |

Rank correlations between dissimilarity indices and gradient separation.

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

`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` . |

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.

Returns a named vector of rank correlations.

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.

Jari Oksanen

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

`vegdist`

, `stepacross`

,
`no.shared`

, `isoMDS`

,
`cor`

, `Machine`

, and for
alternatives `anosim`

, `mantel`

and
`protest`

.

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 1.16-32 Index]