

_D_i_s_t_a_n_c_e _M_a_t_r_i_x _C_o_m_p_u_t_a_t_i_o_n

     dist(x, method = "euclidian", diag = FALSE, upper=FALSE)

     print.dist(dist.obj, diag = NULL, upper = NULL)
     as.matrix.dist(dist.obj)

_A_r_g_u_m_e_n_t_s:

       x: a matrix or (data frame).  Distances between the
          rows of `x' will be computed.

  method: the distance measure to be used. This must be one
          of `"euclidian"', `"maximum"', `"manhattan"',
          `"canberra"' or `"binary"'.  Any unambiguous sub-
          string can be given.

    diag: a logical value indicating whether the diagonal of
          the distance matrix should be printed by
          `print.dist'.

   upper: a logical value indicating whether the upper tri-
          angle of the distance matrix should be be printed
          by `print.dist'.

_D_e_s_c_r_i_p_t_i_o_n:

     This function computes and returns the distance matrix
     computed by using the specified distance measure to
     compute the distances between the rows of `x'.

     Available distance measures are (written for two vec-
     tors x and y):

        * Euclidean: Usual square distance between the two
          vectors (2 norm).

        * Maximum: Maximum distance between two components
          of x and y (supremum norm)

        * Manhattan: Absolute distance between the two vec-
          tors (1 norm).

        * Canberra: sum(|x_i - y_i| / |x_i + y_i|)

        * Count the number of different bits in x and y
          where at least one of the two bits is 1, i.e.,
          components where both bits are zero are ignored.

_V_a_l_u_e:

     The lower triangle of the distance matrix stored by
     columns in a single vector.  The vector has the attri-
     butes `"Size"', `"Diag"', `"Upper"', `"Labels"' and
     `"class"' equal to `"dist"'.

_R_e_f_e_r_e_n_c_e_s:

     Mardia, K. V., J. T. Kent and J. M. Bibby (1979).  Mul-
     tivariate Analysis, London: Academic Press.

_S_e_e _A_l_s_o:

     `hclust'.

_E_x_a_m_p_l_e_s:

     x<-matrix(rnorm(100),nrow=5)
     dist(x)
     dist(x, diag = TRUE)
     dist(x, upper = TRUE)
     as.matrix(dist(x))

