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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)
## S3 method for class 'mst':
plot(x, graph = "circle", x1 = NULL, x2 = NULL, \dots)"dist"."mst" (e.g. returned by mst())."circle" where
the observations are plotted regularly spaced on a circle, and
"nsca" where the two first axex1 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, plotlibrary(stats)
n <- 20
X <- matrix(runif(n * 10), n, 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])
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