distribution: Generate distribution plots of Monte Carlo simulations

In EvalEst: Dynamic Systems Estimation - Extensions

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)))
     ## Default S3 method:
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.

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).

Value

None

See Also

tfplot.MonteCarloSimulations

Examples

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

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