EvalEst (version 2012.4-1)

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(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

A list of TSestModels.

data

data from which to build models.

max.lag

number of lags in the estimated models.

uinput

Input data to umodel.

Value

• A TSdata object.

concept

DSE

Details

This function generates test data using specified models. umodel is used to generate data corresponding to input data and ymodel is used to generate data corresponding to 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 umodel requires input data it should be specified in uinput 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 to rng 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 TSestModels, one for each series in inputData(data) estimated by estVARXls with max.lag lags. genMineData then uses 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="dse")
    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)