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inertdiss               package:ClustGeo               R Documentation

_P_s_e_u_d_o _i_n_e_r_t_i_a _o_f _a _c_l_u_s_t_e_r

_D_e_s_c_r_i_p_t_i_o_n:

     The pseudo inertia of a cluster is calculated from a dissimilarity
     matrix and not from a data matrix.

_U_s_a_g_e:

     inertdiss(D, indices = NULL, wt = NULL)
     
_A_r_g_u_m_e_n_t_s:

       D: an object of class "dist" with the dissimilarities between
          the n observations.  The function 'as.dist' can be used to
          transform an object of class matrix to object of class
          "dist".

 indices: a vector with the indices of the subset of observations.

      wt: vector with the weights of the n observations

_R_e_f_e_r_e_n_c_e_s:

     M.chavent, V. Kuentz-Simonet, A. Labenne, J. Saracco.  ClustGeo:
     an R package for hierarchical clustering with spatial constraints
     arXiv:1707.03897 [stat.CO]

_E_x_a_m_p_l_e_s:

     data(estuary)
     n <- nrow(estuary$dat)
     Z <- scale(estuary$dat)*sqrt(n/(n-1))
     inertdiss(dist(Z)) # pseudo inertia
     inert(Z) #equals for euclidean distance
     
     w <- estuary$map@data$POPULATION # non uniform weights 
     inertdiss(dist(Z),wt=w)
     

inert                 package:ClustGeo                 R Documentation

_I_n_e_r_t_i_a _o_f _a _c_l_u_s_t_e_r

_D_e_s_c_r_i_p_t_i_o_n:

     Computes the inertia of a cluster i.e. on a subset of rows of a
     data matrix.

_U_s_a_g_e:

     inert(Z, indices = 1:nrow(Z), wt = rep(1/nrow(Z), nrow(Z)), M = rep(1,
       ncol(Z)))
     
_A_r_g_u_m_e_n_t_s:

       Z: matrix data

 indices: vector representing the subset of rows

      wt: weight vector

       M: diagonal distance matrix

_E_x_a_m_p_l_e_s:

     data(estuary)
     n <- nrow(estuary$dat)
     Z <- scale(estuary$dat)*sqrt(n/(n-1))
     inert(Z) # number of variables
     
     w <- estuary$map@data$POPULATION # non uniform weights 
     inert(Z,wt=w)
     

plot.choicealpha           package:ClustGeo            R Documentation

_P_l_o_t _o_f _t_h_e _m_i_x_i_n_g _p_a_r_a_m_e_t_e_r

_D_e_s_c_r_i_p_t_i_o_n:

     Plot of the criterion Q or Qnorm obtained in output of the
     function 'choicealpha'.

_U_s_a_g_e:

     ## S3 method for class 'choicealpha'
     plot(x, norm = FALSE, lty = 1:2, pch = c(8, 16),
       type = c("b", "b"), col = 1:2, xlab = "alpha", ylab = NULL,
       legend = NULL, cex = 1, ...)
     
_A_r_g_u_m_e_n_t_s:

       x: an object of class 'choicealpha'.

    norm: if TRUE, the criterion Qnorm is plotted. Otherwise, it is Q.

     lty: a vector of size 2 with the line types of the two curves. See
          par

     pch: a vector of size 2 specifying the symbol for the points of
          the two curves. See par

    type: a vector of size 2 specifying the type of lines of the two
          curves. See par

     col: a vector of size 2 specifying the colors the two curves. See
          par

    xlab: the title fot the x axis.

    ylab: the title fot the y axis.

  legend: a vector of size two the the text for the legend of the two
          curves.

     cex: text size in the legend.

     ...: further arguments passed to or from other methods.

_S_e_e _A_l_s_o:

     'choicealpha'

_E_x_a_m_p_l_e_s:

     data(estuary)
     D0 <- dist(estuary$dat)
     D1 <- as.dist(estuary$D.geo) # the geographic distances between the cities
     range.alpha <- seq(0,1,0.1)
     K <- 5
     cr <- choicealpha(D0,D1,range.alpha,K,graph=FALSE)
     plot(cr,cex=0.8,norm=FALSE,cex.lab=0.8,ylab="pev",
              col=3:4,legend=c("socio-demo","geo"), xlab="mixing parameter")
     plot(cr,cex=0.8,norm=TRUE,cex.lab=0.8,ylab="pev",
              col=5:6,pch=5:6,legend=c("socio-demo","geo"), xlab="mixing parameter")
     

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