genMineData               package:dse2               R Documentation

_G_e_n_e_r_a_t_e _D_a_t_a

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

     Generate data for Monte Carlo experiments

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

         genMineData(umodel, ymodel, uinput=NULL, sampleT=100, 
           unoise=NULL, usd=1,ynoise=NULL, ysd=1, rng=NULL)
         build.input.models(all.data, max.lag=NULL)
         build.diagonal.model(multi.models)

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

  umodel: Model for input data.

  ymodel: Model for output data.

 sampleT: Number of periods of data to generate.

  unoise: Input noise.

     usd: Standard deviationof input noise.

  ynoise: Output noise.

     ysd: Standard deviation of output noise.

     rng: RNG setting.

multi.models: 

all.data: 

 max.lag: 

  uinput: 

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

     This function generates test data using specified models. umodel
     is used to generate data input data and ymodel is used to generate
     data corresponding output data. The result of umodel is used as
     input to ymodel so the input dimension of ymodel should be the
     output dimension of umodel. Typically the ymodel would be
     degenerate in some of the input variables so the effective inputs 
     are a subset. If noise is NULL then an normal noise will be
     generated by simulate. This will be iid N(0,I). The RNG will be
     set first if it is specified.  If unoise or ynoise are specified
     they should be as expected by simulate for the specified umodel
     and ymodel.

     'genMineData' uses 'build.input.models' which makes a list of
     univariate  models, one for each series in 'inputData(data)' and 
     'build.diagonal.model' which builds one diagonal model from a list
      of models returned  by 'build.input.models'. It uses the AR part
     only.

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

     A TSdata object.

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

     'simulate'

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

         if(is.R()) data("eg1.DSE.data.diff", package="dse1")
         umodel <- build.diagonal.model(
                 build.input.models(eg1.DSE.data.diff, max.lag=2))
         z  <- TSdata(output=outputData(eg1.DSE.data.diff), 
                      input = inputData(eg1.DSE.data.diff))
         ymodel <- TSmodel(estVARXls(z, max.lag=3))   
         sim.data <- genMineData(umodel, ymodel)

