is.MonteCarloSimulations(obj)
MonteCarloSimulations(model, simulation.args=NULL,
replications=100, rng=NULL, quiet =FALSE, ...)
## Default S3 method:
MonteCarloSimulations(model, simulation.args = NULL,
replications = 100, rng = NULL, quiet =FALSE, ...)
## S3 method for class 'TSmodel'
MonteCarloSimulations(model, simulation.args=NULL,
replications=100, rng=NULL, quiet=FALSE, ...)
## S3 method for class 'TSestModel'
MonteCarloSimulations(model, simulation.args=NULL,
replications=100, rng=NULL, quiet=FALSE, ...)
## S3 method for class 'EstEval'
MonteCarloSimulations(model, simulation.args=NULL,
replications=100, rng=getRNG(model), quiet=FALSE, ...)
## S3 method for class 'MonteCarloSimulations'
MonteCarloSimulations(model,
simulation.args=NULL, replications=100, rng=getRNG(model), quiet=FALSE, ...)
Arguments
model
an object from which a model can be extracted. The model must
have an associated simulation method (e.g. a TSmodel).
simulation.args,
A list of arguments in addition to model which are passed to simulate.
replications
The number of simulations.
rng
The RNG and starting seed.
quiet
logical indicating if printing and many warning messages should
be suppressed.
obj
an object.
...
arguments passed to other methods.
Details
This function runs many simulations using simulate.
Often it not be necessary to do this since the seed can be used to
reproduce the sample and many functions for testing estimation methods, etc.,
will produce samples as they proceed. This function is useful for verification
and for looking at the stochastic properties of the output of a model.
If model is an object of class EstEval or
simulation
then the model and the seed!!! are extracted so the same sample will be
generated. The default method expects the result of simulate(model) to be
a matrix.
There is a tfplot method (time series plots of the simulations) and a
distribution method for the result. The latter plots kernel estimates
of the distribution of the simulations at specified periods.
Value
A list of simulations.
See Also
simulateEstEvaldistributionforecastCovWRTtrue
Examples
data("eg1.DSE.data.diff", package="dse")
model <- estVARXls(eg1.DSE.data.diff)
z <- MonteCarloSimulations(model, simulation.args=list(sampleT=100))
tfplot(z)
distribution(z)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(EvalEst)
Loading required package: tfplot
Loading required package: tframe
Loading required package: dse
Attaching package: 'dse'
The following objects are masked from 'package:stats':
acf, simulate
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/EvalEst/MonteCarloSimulations.Rd_%03d_medium.png", width=480, height=480)
> ### Name: MonteCarloSimulations
> ### Title: Generate simulations
> ### Aliases: MonteCarloSimulations MonteCarloSimulations.default
> ### MonteCarloSimulations.TSmodel MonteCarloSimulations.TSestModel
> ### MonteCarloSimulations.EstEval
> ### MonteCarloSimulations.MonteCarloSimulations is.MonteCarloSimulations
> ### Keywords: ts
>
> ### ** Examples
>
> data("eg1.DSE.data.diff", package="dse")
> model <- estVARXls(eg1.DSE.data.diff)
> z <- MonteCarloSimulations(model, simulation.args=list(sampleT=100))
> tfplot(z)
> distribution(z)
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.
