nmds                 package:labdsv                 R Documentation

_N_o_n_m_e_t_r_i_c _M_u_l_t_i_d_i_m_e_n_s_i_o_n_a_l _S_c_a_l_i_n_g

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

     This function is simply a wrapper for the isoMDS function in the
     MASS package by Venables and Ripley.  The purpose is to establish
     a nmds class to simplify plotting and additional graphical
     analysis as well as a summary.

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

     nmds(dis,k=2,y=cmdscale(d=dis,k=k),maxit=50)
     bestnmds(dis,k=2,itr=20,maxit=100)

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

     dis: a dist object returned from 'dist()' or a full symmetric
          dissimilarity or distance matrix

       k: the desired number of dimensions for the result

       y: a matrix of initial locations (objects as rows, coordinates
          as columns, as many columns as specified by k).  If none is
          supplied, 'cmdscale' is used to generate them

   maxit: the maximum number of iterations

     itr: number of random starts to find best result

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

     The nmds function simply calls the 'isoMDS()' function of the 
     MASS library, but converts the result from a list to an object of
     class "nmds."  The only purpose for the function is to allow
     'plot', 'identify', 'surf', and other additional methods to be
     defined for the  nmds class, to simplify the analysis of the
     result.

     The 'bestnmds' function runs 'itr' number of random initial
     locations and returns the best result of the set.

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

     an object of class 'nmds', with components: 

  points: the coordinates of samples along axes

  stress: the "goodness-of-fit" computed as stress in percent

_N_o_t_e:

     nmds is included as part of the LabDSV package to provide a
     consistent interface and  utility for vegetation ordination
     methods.  Other analyses included with the same interface include
     principal components analysis (pca), principal coordinates
     analysis (pco), correspondence analysis (ca), canonical
     correspondence analysis (cca), and fuzzy set ordination (fso).

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

     Venables and Ripley for the original isoMDS function

     David W. Roberts droberts@montana.edu <URL:
     http://ecology.msu.montana.edu/labdsv> for the nmds class and
     interface

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

     Kruskal, J.B. (1964)  Multidimensional scaling by optimizing
     goodness of fit to  nonmetric hypothesis.  Psychometrics 29:1-27.

     Kruskal, J.B. (1964)  Nonmetric multidimensional scaling:  a
     numerical method. Psychometrics 29:115-129.

     T.F. Cox and M.A.A. Cox. (1994) _Multidimensional Scaling._
     Chapman and Hall.

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

     'isoMDS' for the original function

     'plot.nmds' for the plot method

     'points.nmds' for a points method

     'plotid.nmds' for a function to identify points in the graphic

     'surf.nmds' for a function to plot contours of independent
     variables on top of the graphic (using the 'gam' and 'interp'
     functions  to calculate the surface

     'jsurf.nmds' for a function to plot contours of independent
     variables on top of the graphic (using the 'gam()' and 'interp()'
     functions  to calculate the surface.  Jitters the coordinates of
     points on the axes to avoid errors in the embedded 'interp'
     function.

     'bestnmds' for a way to automate multiple ramdom starts and save
     the  best result

     'initMDS' for an alternative way to automate random starts

     'postMDS' for a post-solution rescaling

     'metaMDS' for a full treatment of variations

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

         data(bryceveg)
         data(brycesite)
         dis.man <- dist(bryceveg,method="manhattan")
         demo.nmds <- nmds(dis.man,k=4)
         plot(demo.nmds)
         data(brycesit)
         points(demo.nmds,brycesite$elev>8000)
         plotid(demo.nmds)

