distconnected
finds groups that are connected
disregarding dissimilarities that are at or above a threshold or
NA
. The function can be used to find groups that can be
ordinated together or transformed by
stepacross
. Function no.shared
returns a logical
dissimilarity object, where TRUE
means that sites have no
species in common. This is a minimal structure for
distconnected
or can be used to set missing values to
dissimilarities.
distconnected(dis, toolong = 1, trace = TRUE)
no.shared(x)
NA
.
The function uses a fuzz factor, so
that dissimilarities close to the limit will be made NA
, too.
If toolong = 0
(or negative), no dissimilarity is regarded
as too long.
distconnected
distconnected
returns a vector for
observations using integers to identify connected groups. If the data
are connected, values will be all 1
. Function no.shared
returns an object of class dist
.
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).
vegdist
or dist
for getting
dissimilarities, stepacross
for a case where you may need
distconnected
, and for connecting points spantree
.
## There are no disconnected data in vegan, and the following uses an ## extremely low threshold limit for connectedness. This is for ## illustration only, and not a recommended practice. data(dune) dis <- vegdist(dune) gr <- distconnected(dis, toolong=0.4) # Make sites with no shared species as NA in Manhattan dissimilarities dis <- vegdist(dune, "manhattan") is.na(dis) <- no.shared(dune)
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