gen.gev {extRemes} | R Documentation |
Generates data from a GEV (GPD) using 'runif' (and 'rexp') function. May also incorporate a linear trend in location parameter of GEV.
gen.gev(p, n, trend = NULL) gen.gpd(n,sigma,xi,u)
p |
A 1 X 3 vector indicating the mean, scale and shape of the GEV, respectively. |
n |
The sample size to generate. |
trend |
Slope of the location parameter trend (if desired). |
sigma |
Scale parameter of GPD. |
xi |
Shape parameter of GPD. |
u |
Threshold for GPD. |
Value returned (with no trend) is derived from the follwing formula (GEV). mu + sigma (X**(-xi - 1))*xi, where X is a uniform random variable.
For GPD the formula is: sigma/xi * ((1-runif(n))**(-xi-1) for xi != 0 and rexp(n, rate=1/sigma for xi = 0.
Returns a vector of simulated data.
Functions written by Eric Gilleland and Greg Young.
Coles, Stuart. "An introduction to statistical modeling of extreme values", Springer-Verlag (London), 2001.
Gilleland, Eric and Katz, Richard W. Tutorial for the 'Extremes Toolkit: Weather and Climate Applications of Extreme Value Statistics.' http://www.assessment.ucar.edu/toolkit, 2005.
From ismev
package: gev.fit
, gev.diag
,
gpd.fit
, gpd.diag
# obtain a GEV with mean, 4, scale 1.5 and shape of -0.1 mu <- 4 # location parameter sigma <- 1.5 # scale parameter xi <- -0.1 # shape parameter params <- c( mu, sigma, xi) # generate a sample of size 25 gen1 <- gen.gev( p=params, n=25) # Now generate one with a trend. gen2 <- gen.gev( p=params, n=25, trend=0.1) # Fit 'gen1' to a GEV distribution and plot the diagnostics. gen1.fit <- gev.fit( gen1) class( gen1.fit) <- "gev.fit" plot( gen1.fit) # Fit 'gen2' to a GEV distribution and plot the diagnostics. gen2.fit1 <- gev.fit( gen2) class( gen2.fit1) <- "gev.fit" plot( gen2.fit1)