distribution: Generate distribution plots of Monte Carlo simulations

Description

Generate distribution plots of Monte Carlo simulations.

Usage

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

Value

None

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.

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).

Examples

Run this code

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