ssw                  package:spdep                  R Documentation

_C_o_m_p_u_t_e _t_h_e _s_u_m _o_f _d_i_s_s_i_m_i_l_a_r_i_t_y

_D_e_s_c_r_i_p_t_i_o_n:

     This function computes the sum of dissimilarity between each 
     observation and the mean (scalar of vector) of the observations.

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

     ssw(data, id, method = c("euclidean", "maximum", 
         "manhattan", "canberra", "binary", "minkowski", "mahalanobis", 
         "other"), p = 2, cov, inverted = FALSE, otherfun)

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

    data: A matrix with observations in the nodes.

      id: Node index to compute the cost

  method: Character for declare the distance method.  For "euclidean",
          "maximum", "manhattan", "canberra",  "binary" and
          "minkowisk", see 'dist' for details,  because this function
          as used to compute the distance. If 'method="mahalanobis"',
          the mahalanobis distance is computed between neighbour areas.
          If 'method="other"', any function must be informed in
          'otherfun' argument.

       p: The power of the Minkowski distance.

     cov: The covariance matrix used to compute the mahalanobis 
          distance.

inverted: logical.  If 'TRUE', 'cov' is supposed to contain the inverse
          of the covariance matrix.

otherfun: A user defined function to compute the distance

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

     A numeric, the sum of dissimilarity between the observations  'id'
     of 'data' and the mean (scalar of vector) of  this observations.

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

     Elias T. Krainski and Renato M. Assuncao

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

     See Also as 'nbcost'

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

     data(USArrests)
     n <- nrow(USArrests)
     ssw(USArrests, 1:n)
     ssw(USArrests, 1:(n/2))
     ssw(USArrests, (n/2+1):n)
     ssw(USArrests, 1:(n/2)) + ssw(USArrests, (n/2+1):n)

