distribution {dse2} | R Documentation

## 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)), ...)
         ## Default S3 method:
         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)
`...` | arguments passed to other 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.

### 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.

### Value

None

### See Also

`EstEval`

### Examples

    if(is.R()) 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)

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