simulateMPP        package:MarkedPointProcess        R Documentation

_S_i_m_u_l_a_t_i_o_n _o_f _m_a_r_k_e_d _p_o_i_n_t _p_r_o_c_e_s_s_e_s

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

     'simulateMPP' generates realisations of marked point processes

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

     simulateMPP(coordmodel=c("given", "uniform", "Poisson"),
                  coord=NULL, npoints=NULL, lambda=NULL,
                  window=NULL,  edgecorrection=0.0,
                  repetitions=1, coordrepet=1, model=NULL,
                  register=0, method=NULL) 

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

coordmodel: if 'coordmodel="given"' then 'coord' are expected to be
          given and not simulated; if 'coordmodel="uniform"' then
          'npoints' uniformly distributed points are created; if
          'coordmodel="Poisson"' then a conditional Poisson point
          process is simulated with intensity 'lambda'

   coord: matrix with 2 columns; coordinates of the points; 'coord' is
          given only if 'coordmodel="given"'

 npoints: number of coordinates; 'npoints' must be given if
          'coordmodel="uniform"'. 

  lambda: intensity of the Poisson process; 'lambda' must be given if
          'coordmodel="Poisson"'.

  window: '= c(xlim, ylim)'. 'window' must be given if 'coordmodel'
          equals '"uniform"' or '"Poisson"'. 

edgecorrection: double. If 'edgecorrection'>0 then a Poisson process is
          simulated with intensity 'lambda' in a frame of thickness
          'edgecorrection' around the 'window'. If 'window' is not
          given, the range of the x values and the range of the y
          values are taken to define the 'window'. If 'lambda' is not
          given, the intensity within the 'window' is used instead.

repetitions: integer; number of independent drawings of the marks for a
          given set of coordinates

coordrepet: number of independent drawing of the coordinates; this
          parameter is ignored in case of 'coordmodel="given"'

   model: list of lists; model for the marks; see Details and
          'get.mpp.names'. 

register: the register where intermediate results in the Gaussian
          random field simulation are stored, see 'GaussRF'

  method: the method by which the Gaussian random field is simulated;
          if 'is.null(method)' then the method is chosen automatically,
          see 'GaussRF' 

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

     The definition of a model is of the form 'model = list(l.1, OP.1,
     l.2, OP.2, ..., l.n)'. The lists 'l.i' are all either of the  form
     'l.i = list(model=,var=,kappas=,scale=)' or of the form 'l.i =
     list(model=,var=,kappas=,aniso=)' in case of random field parts,
     or of the form 'l.i = list(model=,param=)' in case of marked point
     process parts. 'l.i$model' is a string; 'var' gives the variance;
     'scale' is a scalar whereas 'aniso' is a d x d matrix, which is
     multiplied from left to the points, and at the transformed points
     the values of the random field are calculated.  The dimension d of
     matrix must match the number of rows of 'x'.  'param' is vector of
     real values whose length depends on the specified 'model'.  The
     models for the random field part can be combined by 'OP.i="+"' or
     'OP.i="*"', those for the marked point process parts only by
     'OP.i="+"'.

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

     'coordrepet=1'
      the function returns 'list(coord, data)', 'data' contains the
     independent drawing of the marks (as columns)

     'coordrepet>1'
      the function returns 'list( list(coord, data), ..., list(coord,
     data) )'

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

     Martin Schlather, martin.schlather@math.uni-goettingen.de <URL:
     http://www.stochastik.math.uni-goettingen.de/institute>

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

     'get.mpp.names', 'rfm.test', 'simulateMPP', 'splitmodel',
     MarkedPointProcess

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

      xlim <- c(0, if (interactive()) 200 else 20)
      mpp <- simulateMPP(coordmodel="Poisson", lambda=1,
                   window=c(xlim=xlim, ylim=c(20, 70)),
                   repet=3, coordrepet=4,
                   model=list(list(model="exp", var=1, scale=10),
                              "+",
                              list(model="nearest neighbour", p=1)))
       str(mpp)

