lmom (version 2.8)

evplot: Extreme-value plot

Description

evplot draws an “extreme-value plot”, i.e. a quantile-quantile plot in which the horizontal axis is the quantile of an extreme-value type I (Gumbel) distribution.

evdistp adds the cumulative distribution function of a distribution to an extreme-value plot.

evdistq adds the quantile function of a distribution to an extreme-value plot.

evpoints adds a set of data points to an extreme-value plot.

Usage

evplot(y, ...)
# S3 method for default
evplot(y, qfunc, para, npoints = 101, plim, xlim = c(-2, 5),
       ylim, type,
       xlab = expression("Reduced variate,  " * -log(-log(italic(F)))),
       ylab = "Quantile", rp.axis = TRUE, ...)
evdistp(pfunc, para, npoints = 101, ...)
evdistq(qfunc, para, npoints = 101, ...)
evpoints(y, ...)

Arguments of cumulative distribution functions and quantile functions

pfunc and qfunc can be either the standard R form of cumulative distribution function or quantile function (i.e. for a distribution with \(r\) parameters, the first argument is the variate \(x\) or the probability \(p\) and the next \(r\) arguments are the parameters of the distribution) or the cdf… or qua… forms used throughout the lmom package (i.e. the first argument is the variate \(x\) or probability \(p\) and the second argument is a vector containing the parameter values).

Examples

# NOT RUN {
# Extreme-value plot of Ozone from the airquality data
data(airquality)
evplot(airquality$Ozone)

# Fit a GEV distribution and add it to the plot
evdistq(quagev, pelgev(samlmu(airquality$Ozone)))

# Not too good -- try a kappa distribution instead
evdistq(quakap, pelkap(samlmu(airquality$Ozone)), col="red")
# }