isomds                package:pcurve                R Documentation

_K_r_u_s_k_a_l'_s _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:

     One form of non-metric multidimensional scaling

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


     isomds(d, dim=2, y = cmdscale(d, dim), maxit = 50, trace = TRUE)

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

       d: distance structure of the form returned by `dist' or
          `pcdists', or a full, symmetric matrix.  Data are assumed to
          be dissimilarities or relative distances, but must be
          positive except for self-distance.

     dim: Number of dimensions in solution.

       y: An initial configuration. If none is supplied, `cmdscale' is
          used to provide the classical solution.

   maxit: The maximum number of iterations.

   trace: Logical for tracing optimization. Default TRUE.

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

     This chooses a k-dimensional (default k = 2) configuration to
     minimize the stress, the square root of the ratio of the the sum
     of squared differences between the input distances and those of
     the configuration to the sum of configuration distances squared.
     However, the input distances are allowed a monotonic
     transformation.

     An iterative algorithm is used, which will usually converge in
     around 10 iterations. As this is necessarily an O(n^2)
     calculation, it is slow for large datasets. Further, since the
     configuration is only determined up to rotations and reflections
     (by convention the centroid is at the origin), the result can vary
     considerably from machine to machine.

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

     Two components:

  points: A matrix of the fitted configuration.

  stress: The final stress achieved (in percent).

_S_i_d_e _E_f_f_e_c_t_s:

     If trace is true, the initial stress and the current stress are
     printed out every 5 iterations.

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

     W. N. Venables and B. D. Ripley as part of their MASS library. 
     Minor changes to code for inclusion in pcurve library by Glenn
     De'ath glenn.death@jcu.edu.au. R port by Chris Walsh
     Chris.Walsh@sci.monash.edu.au

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

     T. F. Cox and M. A. A. Cox (1994) Multidimensional Scaling.
     Chapman & Hall.

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

     `cmdscale', `sammon', `pcurve', `dist', `pcdists'

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


     #calculate Bray-Curtis dissimilarity matrix of 45 sites
     #based on grassland plant community species and
     #calculate and plot MDS from it.
         data(soilspec)
         species <- sqrt(soilspec[,2:9])
         specdist <- pcdists(species, metric = "man", scale = FALSE)
         specmds <- isomds(specdist$dnew)
         plot(specmds$points, type = "n", xlab = "MDS axis 1", 
              ylab = "MDS axis 2") 
         text(specmds$points[,1], specmds$points[,2], 
              soilspec$site) 
         mtext(paste("Grassland communities in 45 sites, Stress = ",  
                     0.01*round(specmds$stress, 1)))

