expplot: Classical "exponential distribution" plot

In Renext: Renewal Method for Extreme Values Extrapolation

Classical "exponential distribution" plot

Description

Plot a vector using "exponential distribution" scales

Usage

expplot(x,
        plot.pos = "exp",
        rate = NULL,
        labels = NULL,
        mono = TRUE,
        ...)

Arguments

x	The vector to be plotted.
plot.pos	Plotting position for points: either "exp" for expected ranks or "med" for a median rank approximation (see Details below).
rate	Rate parameter for one or several "exponential distribution" lines to be plotted
labels	Text to display in legend when "exponential distribution" lines are specified
mono	Monochrome graph?
...	Arguments to be passed to plot.

Details

This plot shows -\log[1-F(x)] against x where F(x) at point i is taken as i/(n+1) if plot.pos is "exp", or as the "median rank" approximation (i-0.3)/(n+0.4) if plot.pos is "med".

If the data in x is a sample from an exponential distribution, the points should be roughly aligned. However the largest order statistics have high sampling dispersion.

Note

The log scale for y is emulated via the construction of suitable graduations. So be careful when adding graphical material (points, etc) to this graph with functions of the "add to plot" family (points, lines, ...).

The ML estimate of the rate parameter is the inverse of the sample mean.

Author(s)

Yves Deville

See Also

The weibplot function for a classical "Weibull" plot. The interevt is useful to compute interevents (or "interarrivals") that should follow an exponential distribution in the homogeneous Poisson process context.

Examples

 x <- rexp(200)
 expplot(x, rate = 1/mean(x), labels = "fitted")

Renext documentation built on Aug. 30, 2023, 1:06 a.m.