proflik                 package:geoR                 R Documentation

_C_o_m_p_u_t_e_s _P_r_o_f_i_l_e _L_i_k_e_l_i_h_o_o_d_s

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

     Computes profile likelihoods for model parameters previously
     estimated using the function 'likfit'.

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

     proflik(obj.likfit, geodata, coords = geodata$coords,
             data = geodata$data, sill.values, range.values,
             nugget.values, nugget.rel.values, lambda.values, 
             sillrange.values = TRUE, sillnugget.values = TRUE,
             rangenugget.values = TRUE, sillnugget.rel.values = FALSE,
             rangenugget.rel.values = FALSE, silllambda.values = FALSE,
             rangelambda.values = TRUE,  nuggetlambda.values = FALSE,
             nugget.rellambda.values = FALSE,
             uni.only = TRUE, bi.only = FALSE, messages, ...)

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

obj.likfit: an object of the class 'likfit', typically an output of the
          function 'likfit'.

 geodata: a list containing elements 'coords' and 'data' described
          next. Typically an object of the class '"geodata"' - a 'geoR'
          data-set. If not provided the arguments 'coords' and 'data'
          must be provided instead.  

  coords: an n x 2 matrix containing in each row Euclidean coordinates
          of the n data locations. By default it takes the element
          'coords' of the argument 'geodata'.  

    data: a vector with data values. By default it takes the element
          'data' of the argument 'geodata'.  

sill.values: set of values of the partial sill parameter sigma^2 for
          which the profile likelihood will be computed.  

range.values: set of values of the range parameter phi for which the
          profile likelihood will be computed.  

nugget.values: set of values of the nugget parameter tau^2 for which
          the profile likelihood will be computed. Only used if the
          model was fitted using the function 'likfit' with the option
          'fix.nugget = FALSE'.  

nugget.rel.values: set of values of the relative nugget parameter
          tauR^2 for which the profile likelihood will be computed.
          Only used if the model was fitted using the function 'likfit'
          with the option 'fix.nugget = FALSE'.  

lambda.values: set of values of the Box-Cox transformation parameter
          lambda for which the profile likelihood will be computed.
          Only to be used if the model was fitted using the function
          'likfit' with the option 'fix.lambda = FALSE'.  

sillrange.values: logical indicating whether or not the 2-D profile
          likelihood should be computed. Only valid if 'uni.only =
          FALSE'.  

sillnugget.values: as above.  

rangenugget.values: as above.  

sillnugget.rel.values: as above.  

rangenugget.rel.values: as above.  

silllambda.values: as above.  

rangelambda.values: as above.  

nuggetlambda.values: as above.  

nugget.rellambda.values: as above.  

uni.only: as above.  

 bi.only: as above.  

messages: logical. Indicates whether status messages should be printed
          on the screen (i.e. current output device) while the function
          is running.  

     ...: additional parameters to be passed to the minimization
          function.  

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

     The functions '.proflik.*' are auxiliary functions used to compute
     the profile likelihoods. These functions are internally called by
     the minimization functions when estimating the model parameters.

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

     An object of the class '"proflik"' which is  a list. Each element
     contains values of a parameter (or a pair of parameters for 2-D
     profiles) and the corresponding value of the profile likelihood.
     The components of the output will vary according to the input
     options.

_N_o_t_e:

        1.  Profile likelihoods for Gaussian Random Fields are usually
           uni-modal.  Unusual or jagged shapes can be due to the lack
           of the convergence in the numerical minimization for
           particular values of the parameter(s).   If this is the case
           it might be necessary to pass 'control' arguments  to the
           minimization functions using the argument .... It's also
           advisable to try the different options for the
           'minimisation.function' argument. See documentation of the
           functions 'optim' and/or 'nlm' for further details.

        2.  2-D profiles can be computed by setting the argument
           'uni.only = FALSE'. However, before computing 2-D profiles
           be sure they are really necessary. Their computation can be
           time demanding since it  is performed on a grid determined
           by the cross-product of the values defining the 1-D
           profiles.

        3.  There is no "default strategy" to find reasonable values
           for the x-axis. They must be found in a "try-and-error"
           exercise. It's recommended to use short sequences in the
           initial attempts. The 'EXAMPLE' section below illustrates
           this.

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

     Paulo Justiniano Ribeiro Jr. paulojus@leg.ufpr.br, 
      Peter J. Diggle p.diggle@lancaster.ac.uk.

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

     Further information on the package 'geoR' can be found at:
      <URL: http://www.leg.ufpr.br/geoR>.

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

     'plot.proflik' for graphical output, 'likfit' for the parameter
     estimation, 'optim' and 'nlm' for further details about the
     minimization functions.

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

     op <- par(no.readonly=TRUE)
     ml <- likfit(s100, ini=c(.5, .5), fix.nug=TRUE)
     ## a first atempt to find reasonable values for the x-axis:
     prof <- proflik(ml, s100, sill.values=seq(0.5, 1.5, l=4),
                     range.val=seq(0.1, .5, l=4))
     par(mfrow=c(1,2))
     plot(prof)
     ## a nicer setting 
     ## Not run: 
     prof <- proflik(ml, s100, sill.values=seq(0.45, 2, l=11),
                     range.val=seq(0.1, .55, l=11))
     plot(prof)
     ## to include 2-D profiles use:
     ## (commented because this is time demanding)
     #prof <- proflik(ml, s100, sill.values=seq(0.45, 2, l=11),
     #                range.val=seq(0.1, .55, l=11), uni.only=FALSE)
     #par(mfrow=c(2,2))
     #plot(prof, nlevels=16)
     ## End(Not run)
     par(op)

