spseg             package:spatialkernel             R Documentation

_I_n_t_e_g_r_a_t_e_d _F_u_n_c_t_i_o_n_s _f_o_r _S_p_a_t_i_a_l _S_e_g_r_e_g_a_t_i_o_n _A_n_a_l_y_s_i_s

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

     Spatial segregation analysis to be performed by a single function
     and presentations by associated 'plot' functions.

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

       spseg(pts, marks, h, opt = 2, ntest = 100, poly = NULL, 
         delta = min(apply(apply(pts, 2, range), 2, diff))/100, 
             proc = TRUE)
       plotcv(obj, ...)
       plotphat(obj, types = unique(obj$marks), sup = TRUE, 
         col = risk.colors(10), 
             breaks = seq(0, 1, length = length(col) + 1), ...)
       plotmc(obj, types = unique(obj$marks), quan = c(0.05, 0.95), 
         sup = FALSE, col = risk.colors(10), 
             breaks = seq(0, 1, length = length(col) + 1), ...)

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

     pts: matrix containing the 'x,y'-coordinates of the point
          locations at which type-specific probabilities estimated.

   marks: numeric/character vector of the types of the point in the
          data.

       h: numeric vector of the kernel smoothing bandwidth at which to
          calculate the cross-validated log-likelihood function.

     opt: integer, 1 to select bandwidth; 2 to calculate type-specific 
          probabilities; and 3 to do the Monte Carlo segregation test.

   ntest: integer with default 100, number of simulations for the 
          Monte Carlo test.

    poly: matrix containing the 'x,y'-coordinates of the polygonal
          boundary of the data.

   delta: spacing distance of grid points at which to calculate the
          estimated type-specific probabilities for 'image' plot.

    proc: logical with default 'TRUE' to print the processing message.

     obj: list of the returning value of 'spseg'

   types: numeric/character types of the marks of data points to plot
          the estimated type-specific probabilities, default to plot
          all types.

     sup: logical with default 'FALSE', if 'TRUE' to superimpose data
          points  on the estimated type-specific probability surface.

    quan: numeric, the pointwise significance levels to add contours to
           'image' plot of the estimated type-specific probability
          surface, with default of 'c(0.05, 0.95)'.

     col: list of colors such as that generated by 'risk.colors'.

  breaks: a set of breakpoints for the 'col': must give one more 
          breakpoint than colour.

     ...: other arguments concerning 'plot' and  'points'

_D_e_t_a_i_l_s:

     'spseg' implements a complete spatial segregation analysis  by
     selecting  bandwidth, calculating the type-specific probabilities,
     and then carrying out the Monte Carlo test of spatial segregation
     and pointwise significance. Some 'plot' functions are also
     provided here so that users  can easily present the results.

     These functions are provided only for the convenience of users.
     Users can instead use individual functions to implement the
     analysis step by  step and plot the diagrams as they wish.

     Examples of how to use 'spseg' and present results using 'plot'
     functions are presented in  'spatialkernel-package'.

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

     'spseg' returns a list with components  

     hcv: bandwidth selected by the cross-validated log-likelihood
          function.

gridx,gridy: 'x, y' coordinate vectors at which the grid points  are
          generated at which to calculate the type-specific
          probabilities  and pointwise segregation test _p_-value.

       p: estimated type-specific probabilities at grid points
          generated by vectors 'gridx, gridy'.

  pvalue: _p_-value of the Monte Carlo spatial segregation test.

stpvalue: pointwise _p_-value of the Monte Carlo spatial  segregation
          test.

     ...: copy of 'pts, marks, h, opt'.

_N_o_t_e:

     Setting 'h' to a unique value may force 'spseg' to skip the
     selecting bandwidth step, go straight to calculate the
     type-specific  probabilities and then test the spatial segregation
     with this fixed value of bandwidth.

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

     'cvloglk', 'phat', 'mcseg.test',  'pinpoly', 'risk.colors', and
     'metre'

