EvalEst (version 2012.4-1)

distribution: Generate distribution plots of Monte Carlo simulations

Description

Generate distribution plots of Monte Carlo simulations.

Usage

distribution(obj, ...)
     ## S3 method for class 'TSdata':
distribution(obj, ..., bandwidth=0.2, 
             select.inputs = seq(length= nseriesInput(obj)),
             select.outputs= seq(length=nseriesOutput(obj)))
     ## S3 method for class 'default':
distribution(obj, ..., bandwidth=0.2, series=NULL)
    ## S3 method for class 'MonteCarloSimulations':
distribution(obj,
        series=seq(dim(obj$simulations)[2]),
        x.sections=TRUE, periods=1:3, graphs.per.page=5, ...)

Arguments

obj

The result of MonteCarloSimulations.

bandwidth

passed to density or ksmooth.

series

The series which should be plotted. The default gives all series.

select.inputs

series to be plotted. (passed to selectSeries)

select.outputs

series to be plotted. (passed to selectSeries)

x.sections

If TRUE then kernel density estimates are plotted for periods indicated by periods. If FALSE then a time series plots of the mean and estimates 1 and 2 standard deviations from the mean. Periods is ignored if x.sections is FALSE.

periods

The periods at which the distribution should be calculated and plotted. The default gives the first three.

graphs.per.page

integer indicating number of graphs to place on a page.

...

(further arguments, currently disregarded).

select

integer vector indicating roots to be plotted. If select is not NULL then roots are sorted by magnitude and only the indicated roots are plotted. For example, select=c(1,2) will plot only the two largest roots.

Value

• None

concept

DSE

Details

Kernel estimates of the densities (series by series, not joint densities) are estimated using ksmooth (if available) or density (if available) to produces density plots. Output graphics can be paused between pages by setting par(ask=TRUE).

See Also

tfplot.MonteCarloSimulations

Examples

data("eg1.DSE.data.diff", package="dse")
model <- estVARXls(eg1.DSE.data.diff)
z <-  MonteCarloSimulations(model)
distribution(z)