dist                   package:mva                   R Documentation

_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

_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 a data matrix.

_U_s_a_g_e:

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

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

_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
          `"euclidean"', `"maximum"', `"manhattan"',  `"canberra"' or
          `"binary"'. Any unambiguous substring 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 triangle of the
          distance matrix should be printed by `print.dist'.

       m: A distance matrix to be converted to a dist object (only
          lower triangle is used, the rest is ignored).

_D_e_t_a_i_l_s:

     Available distance measures are (written for two vectors 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 vectors (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.

     The functions `as.matrix.dist()' and `as.dist()' can be used for
     conversion between objects of class `"dist"' and conventional
     distance matrices and vice versa.

_V_a_l_u_e:

     The lower triangle of the distance matrix stored by columns in a
     single vector.  The vector has the attributes `"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). Multivariate
     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)
     m <- as.matrix(dist(x))
     as.dist(m)

