Data sets are disconnected if they have sample plots or groups of
sample plots which share no species with other sites or groups of
sites. Such data sets cannot be sensibly ordinated by any
unconstrained method because these subsets cannot be related to each
other. For instance, correspondence analysis will polarize these
subsets with eigenvalue 1. Neither can such dissimilarities be
transformed with stepacross
, because there is no path
between all points, and result will contain NA
s. Function
distconnected
will find such subsets in dissimilarity
matrices. The function will return a grouping vector that can be used
for sub-setting the data. If data are connected, the result vector will
be all \(1\)s. The connectedness between two points can be defined
either by a threshold toolong
or using input dissimilarities
with NA
s.
Function no.shared
returns a dist
structure having value
TRUE
when two sites have nothing in common, and value
FALSE
when they have at least one shared species. This is a
minimal structure that can be analysed with distconnected
. The
function can be used to select dissimilarities with no shared species
in indices which do not have a fixed upper limit.
Function distconnected
uses depth-first search
(Sedgewick 1990).