dse (version 2009.7-1)

genMineData: 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

Value

• A TSdata object.

concept

DSE

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.

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)