mst {ape} | R Documentation |
The function 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, ...)
X |
either a matrix that can be interpreted as a distance matrix,
or an object of class |
x |
an object of class |
graph |
a character string indicating the type of graph to plot
the minimum spanning tree; two choices are possible: |
x1 |
a numeric vector giving the coordinates of the observations
on the x-axis. Both |
x2 |
a numeric vector giving the coordinates of the observations
on the y-axis. Both |
... |
further arguments to be passed to |
These functions provide two ways to plot the minimum spanning tree which
try to space as much as possible the observations in order to show as
clearly as possible the links. The option 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.
an object of class "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.
Yvonnick Noel noel@univ-lille3.fr, Julien Claude julien.claude@umontpellier.fr and Emmanuel Paradis
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)