distconnected {vegan} | R Documentation |
Function 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.
Function spantree
finds a minimum spanning tree
connecting all points, but disregarding dissimilarities that are at or
above the threshold or NA
.
distconnected(dis, toolong = 1, trace = TRUE) no.shared(x) spantree(dis, toolong = 0)
dis |
Dissimilarity data inheriting from class dist or
a an object, such as a matrix, that can be converted to a
dissimilarity matrix. Functions vegdist and
dist are some functions producing suitable
dissimilarity data. |
toolong |
Shortest dissimilarity regarded as 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 dissimmilarity is regarded
as too long.
|
trace |
Summarize results of distconnected |
x |
Community data. |
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 subsetting the
data. If data are connected, the result vector will be all
1s. 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 spantree
finds a minimum spanning tree for
dissimilarities (there may be several minimum spanning trees, but the
function finds only one). Dissimilarities at or above the threshold
toolong
and NA
s are disregarded, and the spanning tree
is found through other dissimilarities. If the data are disconnected,
the function will return a disconnected tree (or a forest), and the
corresponding link is NA
. The results of spantree
can be
overlaid onto an ordination diagram using function
ordispantree
.
Function distconnected
uses depth-first search
(Sedgewick 1990). Function spantree
uses Prim's method
implemented as priority-first search for dense graphs (Sedgewick
1990).
Function 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
. Function spantree
returns a list with two vectors, each of length n-1. The
number of links in a tree is one less the number of observations, and
the first item is omitted. The items are
kid |
The child node of the parent, starting from parent number
two. If there is no link from the parent, value will be NA
and tree is disconnected at the node. |
dist |
Corresponding distance. If kid = NA , then
dist = 0 . |
In principle, minimum spanning tree is equivalent to single linkage
clustering that can be performed using hclust
or
agnes
. However, these functions combine
clusters to each other and the information of the actually connected points
(the ``single link'') cannot be recovered from the result. The
graphical output of a single linkage clustering plotted with
ordicluster
will look very different from an equivalent
spanning tree plotted with ordispantree
.
Jari Oksanen
Sedgewick, R. (1990). Algorithms in C. Addison Wesley.
vegdist
or dist
for getting
dissimilarities, stepacross
for a case where you may need
distconnected
, ordispantree
for displaying
results of spantree
, and hclust
or
agnes
for single linkage clustering.
## 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) ord <- cmdscale(dis) ## metric MDS gr <- distconnected(dis, toolong=0.4) tr <- spantree(dis, toolong=0.4) ordiplot(ord, type="n") ordispantree(ord, tr, col="red", lwd=2) points(ord, cex=1.3, pch=21, col=1, bg = gr) # Make sites with no shared species as NA in Manhattan dissimilarities dis <- vegdist(dune, "manhattan") is.na(dis) <- no.shared(dune)