genMineData: Generate Data

In EvalEst: Dynamic Systems Estimation - Extensions

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.

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.

Value

A TSdata object.

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)

EvalEst documentation built on March 18, 2024, 3:01 p.m.