isoMDS                 package:MASS                 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, y = cmdscale(d, k), k = 2, maxit = 50, trace = TRUE,
            tol = 1e-3, p = 2)

     Shepard(d, x, p = 2)

_A_r_g_u_m_e_n_t_s:

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

       y: An initial configuration. If none is supplied, 'cmdscale' is
          used to provide the classical solution, unless there are
          missing or infinite dissimilarities. 

       k: The desired dimension for the solution, passed to 'cmdscale'. 

   maxit: The maximum number of iterations. 

   trace: Logical for tracing optimization. Default 'TRUE'. 

     tol: convergence tolerance. 

       p: Power for Minkowski distance in the configuration space.

       x: A final configuration.

_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 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 for the
     default p = 2 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 two-column vector 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.

_R_e_f_e_r_e_n_c_e_s:

     T. F. Cox and M. A. A. Cox (1994, 2001) _Multidimensional
     Scaling_. Chapman & Hall.

     Ripley, B. D. (1996) _Pattern Recognition and Neural Networks_.
     Cambridge University Press.

     Venables, W. N. and Ripley, B. D. (2002) _Modern Applied
     Statistics with S._ Fourth edition.  Springer.

_S_e_e _A_l_s_o:

     'cmdscale', 'sammon'

_E_x_a_m_p_l_e_s:

     data(swiss)
     swiss.x <- as.matrix(swiss[, -1])
     swiss.dist <- dist(swiss.x)
     swiss.mds <- isoMDS(swiss.dist)
     plot(swiss.mds$points, type = "n")
     text(swiss.mds$points, labels = as.character(1:nrow(swiss.x)))
     swiss.sh <- Shepard(swiss.dist, swiss.mds$points)
     plot(swiss.sh, pch = ".")
     lines(swiss.sh$x, swiss.sh$yf, type = "S")

