rankindex(grad, veg, indices = c("euc", "man", "gow", "bra", "kul"),
stepacross = FALSE, method = "spearman", ...)
vegdist
.
Alternatively, it can be a (named) list of functions returning
objects of class 'dist'.stepacross
to find
a shorter path dissimilarity. The dissimilarities for site pairs
with no shared species are set NA
using
no.shared<
stepacross
.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.
vegdist
, stepacross
,
no.shared
, monoMDS
,
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))
## 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)
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