R: Mean excess plot

meanExcessPlot {laeken} | R Documentation

## Mean excess plot

### Description

The Mean Excess plot is a graphical method for detecting the threshold (scale parameter) of a Pareto distribution.

### Usage

    meanExcessPlot(x, w = NULL, probs = NULL, interactive = TRUE,
      pch = par("pch"), cex = par("cex"), col = par("col"),
      bg = "transparent", ...)

### Arguments

`x` | a numeric vector.
`w` | an optional numeric vector giving sample weights.
`probs` | an optional numeric vector of probabilities with values in [0,1], defining the quantiles to be plotted. This is useful for large data sets, when it may not be desirable to plot every single point.
`interactive` | a logical indicating whether the threshold (scale parameter) can be selected interactively by clicking on points. Information on the selected threshold is then printed on the console.
`pch,cex,col,bg` | graphical parameters for the plot symbol of each data point or quantile (see `points`).
`...` | additional arguments to be passed to `plot.default`.

### Details

The corresponding mean excesses are plotted against the values of `x` (if supplied, only those specified by `probs`). If the tail of the data follows a Pareto distribution, these observations show a positive linear trend. The leftmost point of a fitted line can thus be used as an estimate of the threshold (scale parameter).

The interactive selection of the threshold (scale parameter) is implemented using `identify`. For the usual `X11` device, the selection process is thus terminated by pressing any mouse button other than the first. For the `quartz` device (on Mac OS X systems), the process is terminated either by a secondary click (usually second mouse button or `Ctrl`-click) or by pressing the `ESC` key.

### Value

If `interactive` is `TRUE`, the last selection for the threshold is returned invisibly as an object of class `"paretoScale"`, which consists of the following components:

`x0` | the selected threshold (scale parameter).
`k` | the number of observations in the tail (i.e., larger than the threshold).

### Note

The functionality to account for sample weights and to select the threshold (scale parameter) interactively was introduced in version 0.2.

### Author(s)

Andreas Alfons and Josef Holzer

### See Also

`paretoScale`, `paretoTail`, `minAMSE`, `paretoQPlot`, `identify`

### Examples

    data(eusilc)
    # equivalized disposable income is equal for each household
    # member, therefore only one household member is taken
    eusilc <- eusilc[!duplicated(eusilc$db030),]

    # with sample weights
    meanExcessPlot(eusilc$eqIncome, w = eusilc$db090)

    # without sample weights
    meanExcessPlot(eusilc$eqIncome)

[Package laeken version 0.4.6 Index]