mst finds the minimum spanning tree between a set of
observations using a matrix of pairwise distances. The plot method plots the minimum spanning tree showing the
links where the observations are identified by their numbers.
mst(X)
"plot"(x, graph = "circle", x1 = NULL, x2 = NULL, ...)"dist"."mst" (e.g. returned by mst())."circle" where
the observations are plotted regularly spaced on a circle, and
"nsca" where the two first axes of a non-symmetric correspondence
analysis are used to plot the observations (see Details below). If
both arguments x1 and x2 are given, the argument
graph is ignored.x1 and x2 must be specified
to be used.x1 and x2 must be specified
to be used.plot()."mst" which is a square numeric matrix of size
equal to the number of observations with either 1 if a link
between the corresponding observations was found, or 0
otherwise. The names of the rows and columns of the distance matrix,
if available, are given as rownames and colnames to the returned object.
graph = "circle"
simply plots regularly the observations on a circle, whereas
graph = "nsca" uses a non-symmetric correspondence analysis
where each observation is represented at the centroid of its neighbours.Alternatively, the user may use any system of coordinates for the obsevations, for instance a principal components analysis (PCA) if the distances were computed from an original matrix of continous variables.
dist.dna, dist.gene,
dist, plot
require(stats)
X <- matrix(runif(200), 20, 10)
d <- dist(X)
PC <- prcomp(X)
M <- mst(d)
opar <- par()
par(mfcol = c(2, 2))
plot(M)
plot(M, graph = "nsca")
plot(M, x1 = PC$x[, 1], x2 = PC$x[, 2])
par(opar)
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