genMineData {dse2}R Documentation

Generate Data

Description

Generate data for Monte Carlo experiments

Usage

    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)

Arguments

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

Details

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.

Value

A TSdata object.

See Also

simulate

Examples

    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)

[Package dse2 version 2008.10-1 Index]