prunecost               package:spdep               R Documentation

_C_o_m_p_u_t_e _c_o_s_t _o_f _p_r_u_n_e _e_a_c_h _e_d_g_e

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

     If any edge are dropped, the MST are pruned. This generate a two
     subgraphs. So, it makes a tree graphs and tree dissimilarity
     values are computed, one for each graph. The dissimilarity is the
     sum over sqared differences between the observactions in the nodes
     and mean vector of observations in the graph. The dissimilarity of
     original graph and the sum of dissimilarity of subgraphs are
     returned.

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

     prunecost(edges, data, 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:

   edges: A matrix with 2 colums with each row is one edge

    data: A data.frame with observations in the nodes.

  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 vector with the differences between the dissimilarity of all
     nodes and the dissimilarity sum of all subgraphs obtained by
     pruning one edge each time.

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

     Elias T. Krainski and Renato M. Assuncao

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

     See Also as 'prunemst'

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

     d <- data.frame(a=-2:2, b=runif(5))
     e <- matrix(c(1,2, 2,3, 3,4, 4,5), ncol=2, byrow=TRUE)

     sum(sweep(d, 2, colMeans(d))^2)

     prunecost(e, d)

