mst                   package:ape                   R Documentation

_M_i_n_i_m_u_m _S_p_a_n_n_i_n_g _T_r_e_e

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

     mst(X)
     ## S3 method for class 'mst':
     plot(x, graph = "circle", x1 = NULL, x2 = NULL, ...)

_A_r_g_u_m_e_n_t_s:

       X: either a matrix that can be interpreted as a distance matrix,
          or an object of class '"dist"'.

       x: an object of class '"mst"' (e.g. returned by 'mst()').

   graph: a character string indicating the type of graph to plot the
          minimum spanning tree; two choices are possible: '"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: a numeric vector giving the coordinates of the observations
          on the _x_-axis. Both 'x1' and 'x2' must be specified to be
          used.

      x2: a numeric vector giving the coordinates of the observations
          on the _y_-axis. Both 'x1' and 'x2' must be specified to be
          used.

     ...: further arguments to be passed to 'plot()'.

_D_e_t_a_i_l_s:

     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.

_V_a_l_u_e:

     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.

_A_u_t_h_o_r(_s):

     Yvonnick Noel noel@univ-lille3.fr, Julien Claude
     claude@isem.univ-montp2.fr and Emmanuel Paradis
     paradis@isem.univ-montp2.fr

_S_e_e _A_l_s_o:

     'dist.dna', 'dist.gene', 'dist', 'plot'

_E_x_a_m_p_l_e_s:

     library(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])
     par(opar)

