dissimilarity.object         package:cluster         R Documentation

_D_i_s_s_i_m_i_l_a_r_i_t_y _M_a_t_r_i_x _O_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     Objects of class '"dissimilarity"' representing the dissimilarity
     matrix of a dataset.

_V_a_l_u_e:

     The dissimilarity matrix is symmetric, and hence its lower
     triangle (column wise) is represented as a vector to save storage
     space. If the object, is called 'do', and 'n' the number of
     observations, i.e., 'n <- attr(do, "Size")', then for i < j <= n,
     the dissimilarity between (row) i and j is 'do[n*(i-1) - i*(i-1)/2
     + j-i]'. The length of the vector is n*(n-1)/2, i.e., of order
     n^2.

     '"dissimilarity"' objects also inherit from class 'dist' and can
     use 'dist' methods, in particular, 'as.matrix', such that d(i,j)
     from above is just 'as.matrix(do)[i,j]'.

     The object has the following attributes: 

    Size: the number of observations in the dataset.

  Metric: the metric used for calculating the dissimilarities. 
          Possible values are "euclidean", "manhattan", "mixed" (if
          variables of different types were present in the dataset),
          and "unspecified".

  Labels: optionally, contains the labels, if any, of the observations
          of the dataset.

NA.message: optionally, if a dissimilarity could not be computed,
          because of too many missing values for some observations of
          the dataset.

   Types: when a mixed metric was used, the types for each variable as
          one-letter codes (as in the book, e.g. p.54):

          _A Asymmetric binary

          _S Symmetric  binary

          _N Nominal (factor)

          _O Ordinal (ordered factor)

          _I Interval scaled (numeric)

          _T raTio to be log transformed (positive numeric)

_G_E_N_E_R_A_T_I_O_N:

     'daisy' returns this class of objects. Also the functions 'pam',
     'clara', 'fanny', 'agnes', and 'diana' return a 'dissimilarity'
     object, as one component of their return objects.

_M_E_T_H_O_D_S:

     The '"dissimilarity"' class has methods for the following generic
     functions: 'print', 'summary'.

_S_e_e _A_l_s_o:

     'daisy', 'dist', 'pam', 'clara', 'fanny', 'agnes', 'diana'.

