dse (version 2006.10-1)

distribution: Plot distribution of estimates

Description

Plot distribution of estimates.

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 'coefEstEval':
distribution(obj, ...,  Sort=FALSE, bandwidth=0.2,
	graphs.per.page=5)
     ## S3 method for class 'rootsEstEval':
distribution(obj, ..., mod=TRUE, invert=FALSE, Sort=FALSE,
        bandwidth=0.2, select=NULL)

Arguments

obj

an object as returned by EstEval.

Sort

if Sort is true then sort is applied. This helps (a bit) with estimation methods like black.box which may not return parameters of the same length or in the same order.

bandwidth

passed to density or ksmooth.

graphs.per.page

integer indicating number of graphs to place on a page.

series

series to be plotted. (passed to selectSeries)

select.inputs

series to be plotted. (passed to selectSeries)

select.outputs

series to be plotted. (passed to selectSeries)

...

other objects to be plotted (not working for some methods).

invert

logical indicating if the inverse of roots should be plotted

mod

logical indicating if the modulus of roots should be plotted

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

ksmooth is applied if available to get a smoothed estimate of the distribution of the estimates. If ksmooth is not available then density is applied if it is available.

See Also

EstEval

Examples

data("eg1.DSE.data.diff", package="dse1")
model <- estVARXls(TSdata(output=outputData(eg1.DSE.data.diff)), max.lag=2)
# now use this as the true model
z <-  EstEval(model, 
    estimation="estVARXls", estimation.args=list(max.lag=2))
distribution(z) 
tfplot(z)