prabclust              package:prabclus              R Documentation

_C_l_u_s_t_e_r_i_n_g _o_f _s_p_e_c_i_e_s _r_a_n_g_e_s _f_r_o_m _p_r_e_s_e_n_c_e-_a_b_s_e_n_c_e _m_a_t_r_i_c_e_s

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

     Clusters a presence-absence matrix object by calculating an MDS
     from the distances, and applying maximum likelihood Gaussian
     mixtures clustering with "noise" (package 'mclust') to the MDS
     points. The solution is plotted. A standard execution will be 
      'prabmatrix <- prabinit(file="path/prabmatrixfile",
     neighborhood="path/neighborhoodfile")'
      'clust <- prabclust(prabmatrix)'
      'print(clust)' 
      *Note:* Data formats are described on the 'prabinit' help page.
     You may also consider the example datasets 'kykladspecreg.dat' and
     'nb.dat'. Take care of the parameter 'rows.are.species' of
     'prabinit'.

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

     prabclust(prabobj, mdsmethod = "classical", mdsdim = 4, nnk =
     ceiling(prabobj$n.species/40), nclus = 0:9, modelid = "noVVV")

     ## S3 method for class 'prabclust':
     print(x, bic=FALSE, ...)

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

 prabobj: object of class 'prab' as generated by 'prabinit'.
          Presence-absence data to be analyzed. 

mdsmethod: '"classical"', '"kruskal"', or '"sammon"'. The MDS method to
          transform the distances to data points. '"classical"'
          indicates metric MDS by function 'cmdscale', '"kruskal"' is
          non-metric MDS.

  mdsdim: integer. Dimension of the MDS points.

     nnk: integer. Number of nearest neighbors to determine the initial
          noise estimation by 'NNclean'.

   nclus: vector of integers. Numbers of clusters to perform the
          mixture estimation.

 modelid: string. Model name for 'EMclustN' (see the corresponding help
          page). Additionally, '"noVVV"' is possible, which fits all
          methods except '"VVV"'.

       x: object of class 'prabclust'. Output of 'prabclust'.

     bic: logical. If 'TRUE', information about the BIC criterion to
          choose the model is displayed.

     ...: necessary for summary method.

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

     'print.prabclust' does not produce output.  'prabclust' generates
     an object of class 'prabclust'. This is a list with components 

clustering: vector of integers indicating the cluster memberships of
          the species. Noise can be recognized by output component
          'symbols'.

clustsummary: output object of 'summary.EMclustN'. A list giving the
          optimal (according to BIC) parameters,  conditional
          probabilities `z', and loglikelihood, together with the
          associated classification and its uncertainty.

bicsummary: output object of 'EMclustN'. Bayesian Information Criterion
          for the specified mixture models and numbers of clusters. 

  points: numerical matrix. MDS configuration.

     nnk: see above.

  mdsdim: see above.

mdsmethod: see above.

 symbols: vector of characters, similar to 'clustering', but indicating
          estimated noise and points belonging to one-point-components
          (which should be interpreted as some kind of noise as well)
          by '"N"'. 

_N_o_t_e:

     Note that we used 'mdsmethod="kruskal"' in our publications, but
     we prefer the new default 'mdsmethod="classical"' now, because we
     discovered some numerical instabilities of the
     'isoMDS'-implementation in connection with our distance matrices.

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

     Christian Hennig hennig@math.uni-hamburg.de <URL:
     http://www.math.uni-hamburg.de/home/hennig/>

_R_e_f_e_r_e_n_c_e_s:

     Hennig, C. and Hausdorf, B. (2002) Distance-based parametric
     bootstrap tests for clustering of species ranges, submitted, <URL:
     http://stat.ethz.ch/Research-Reports/110.html>.

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

     'EMclustN', 'summary.EMclustN', 'NNclean', 'cmdscale', 'isoMDS',
     'sammon', 'prabinit'.

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

     data(kykladspecreg)
     # Note: If you do not use the installed package, replace this by
     # kykladspecreg <- read.table("(path/)kykladspecreg.dat")
     data(nb)
     # Note: If you do not use the installed package, replace this by
     # nb <- list()
     # for (i in 1:34)
     #   nb <- c(nb,list(scan(file="(path/)nb.dat",
     #                   skip=i-1,nlines=1)))
     set.seed(1234)
     x <- prabinit(prabmatrix=kykladspecreg, neighborhood=nb)
     # If you want to use your own ASCII data files, use
     # x <- prabinit(file="path/prabmatrixfile",
     # neighborhood="path/neighborhoodfile")
     print(prabclust(x))

