marksum                package:ecespa                R Documentation

_M_a_r_k-_s_u_m _m_e_a_s_u_r_e

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

     An exploratory data analysis technique for marked point patterns.
     The marked point pattern is mapped to a random field for visual
     inspection.

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

     marksum(mippp, R = 10, nx = 30, ny = 30)

     ## S3 method for ploting objects of class 'ecespa.marksum':
     ## S3 method for class 'ecespa.marksum':
     plot(x, what="normalized",  contour=FALSE, grid=FALSE, ribbon=TRUE,col=NULL ,main=NULL,xlab="",ylab="",...)

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

   mippp: A marked point pattern. An object with the 'ppp' format of
          'spatstat'. 

       R: Radius. The distance argument _ r_ at which the mark-sum
          measure should be computed 

      nx: Grid density (for estimation) in the x-side. 

      ny: Grid density (for estimation) in the y-side. 

       x: An object of class ''ecespa.marksum''. Usually, the result of
          applying 'marksum' to a point pattern.

    what: What to plot. One of '"marksum"' (raw mark sum measure),
          '"point"' (point sum measure) or '"normalized"' (normalized
          sum measure).

 contour: Logical; if '"TRUE"' add contour to map.

    grid: Logical; if '"TRUE"' add marked grid to map.

  ribbon: Logical; if '"TRUE"' add legend to map.

     col: Color table to use for the map ( see help file on image for
          details). 

    main: Text or expression to add as a title to the plot.

    xlab: Text or expression to add as a label to axis x.

    ylab: Text or expression to add as a label to axis y.

     ...: Additional parameters to 'smooth.ppp', 'density.ppp' or
          'as.mask', to control  the  parameters of the smoothing
          kernel, pixel resolution, etc. 

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

     Penttinen (2006) defines the _mark-sum measure_ as a smoothed
     summary measuring locally the contribution of points and marks.
     For any fixed location x within the  observational   window and a
     distance R, the mark-sum measure  S[R](x) equals the sum of the
     marks of the points within the circle of radius R with centre in 
     x. The _point-sum measure_  I[R](x) is defined by him as the sum
     of points within the circle of radius R with centre in  x, and
     describes the contribution of points locally near x. The
     _normalized mark-sum measure_ describes the contribution of marks 
     near x and is defined (Penttinen, 2006) as

                 S.normalized[R](x) = S[R](x)/I[R](x)

     This implementation of 'marksum' estimates the mark-sum and the
     point-sum measures in a grid of points whose density is defined by
     'nx' and  'ny'.

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

     'marksum' gives an object of class ''ecespa.marksum''; basically a
     list with the following elements: 

normalized : Normalized mark-sum measure estimated in the grid points. 

marksum : Raw mark-sum measure estimated in the grid points. 

pointsum : Point-sum measure estimated in the grid points. 

   minus: Point-sum of the grid points. For advanced use only.

    grid: Grid of points. 

     nx : Density of the estimating grid  in the x-side. 

     ny : Density of the estimating grid  in the x-side. 

dataname : Name of the ppp object analysed. 

       R: Radius. The distance argument _r_ at which the mark-sum
          measure has been computed. 

  window: Window of the point pattern.


     'plot.ecespa.marksum' plots the selected mark-sum measure.

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

     Marcelino de la Cruz Rot marcelino.delacruz@upm.es

_R_e_f_e_r_e_n_c_e_s:

     Penttinen, A. 2006. Statistics for Marked Point Patterns. In _The
     Yearbook of the Finnish Statistical Society_, pp. 70-91.

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

     'getis', related to the point-sum measure, and  'markstats' for
     designing different implementations.

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

      ## Not run: 
        
      data(seedlings1)
        
      seed.m <- marksum(seedlings1, R=25)

      plot(seed.m, what="marksum", sigma = 5)  # raw mark-sum measure; sigma is bandwith for smoothing

      plot(seed.m, what="pointsum", sigma = 5, col = tim.colors(30),) # point sum measure
        
      plot(seed.m,  what="normalized", dimyx=200, contour=TRUE, sigma = 5) # normalized  mark-sum measure

     # the same with added grid
     plot(seed.m,  what="normalized", dimyx=200, contour=TRUE, sigma = 5, grid=TRUE) # normalized  mark-sum measure

     ## End(Not run)

