splitmodel        package:MarkedPointProcess        R Documentation

_S_p_l_i_t _b_e_t_w_e_e_m _m_a_r_k_e_d _p_o_i_n_t _p_r_o_c_e_s_s_e_s _a_n_d _r_a_n_d_o_m _f_i_e_l_d_s

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

     'splitmodel' splits a model given in form of a list (the third
     variant of model definition for random fields, see
     'CovarianceFct') into a random field part and a marked point
     process part

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

     splitmodel(model)

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

   model: 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 the right to the points, and at the
          transformed points the values of the (isotropic) random field
          (with scale 1) 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:

     'list(RF=RF, mpp=mpp)' where 'RF' is a usual model definition for
     a random field.  Further, 'mpp=list(mpp.1,...,mpp.n)', where
     'mpp.i=list(model=model,param=param,mnr=)' and 'mnr' is the
     internal C code for 'model'.

_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:

     'simulateMPP'

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

     str(splitmodel(list(list(model="exp", var=5, scale=3))))

     str(splitmodel(list(list(model="nearest neighbour", param=4))))

     str(splitmodel(list(list(model="exp", var=5, scale=3),
                         "+",
                         list(model="nearest neighbour", param=4)
                         )))

     str(splitmodel(list(list(model="exp", var=5, scale=3),
                         "*",
                         list(model="spherical", var=1, scale=2),
                         "+",
                         list(model="nearest neighbour", param=4),
                         "+",
                         list(model="random coin",
                              param=c(fct=1, scale=7, height=8))
                         )))

