FitGev: Fit Generalized extreme value (GEV) distribution

In martindurocher/floodStat: FloodNet Regional Frequency Analysis

Description

Fit a GEV distribution on annual maxima using the generalized maximum likelihood method with Beta prior. The output is of the class amax. See FitAmax. Asymptotic result are computed like the maximum likelihood approach.

Usage

FitGev(x, varcov = TRUE, mu = -0.1, sig2 = 0.015,
  method.optim = "BFGS", ...)

Arguments

x	Data.
varcov	Logical. Should the covariance matrix be returned.
mu, sig2	Mean and variance of the Beta prior.
method.optim	Optimisation method used by optim
...	Other parameter pass to optim

References

Martins, E.S., Stedinger, J.R., 2000. Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data. Water Resour. Res. 36, 737–744. https://doi.org/10.1029/1999WR900330

Examples

x <- ExtractAmax(flow~date, flowStJohn)$flow

## Using the default physiographic prior.
fit <- FitGev(x)
print(fit)
coef(fit)
vcov(fit)
predict(fit, ci = 'delta')

## A uniform prior on interlval -0.5 to 0.5 can be used to approximate
## the maximum likelihood estimate.

AIC(FitAmax(x, distr = 'gev', method ='mle'))
AIC(FitGev(x, mu = 0, sig2 = 1/12))

## A regional study can be performed by using an empirical prior
## Here 20 sites with the same GEV distribution are simulated
## without priors.
## Then a regional estimate is obtained using an empirical prior

xmat <- replicate(20, rAmax(20, c(100,3, -.1), 'gev') )
flist <- apply(xmat,2, FitAmax, distr = 'gev', varcov = FALSE)
pmat <- sapply(flist, getElement,'para')

kap0 <- pmin(.5,pmax(-.5,pmat[3,]))

FitGev(xmat[,1], mu = mean(kap0), sig2 = var(kap0))
FitAmax(xmat[,1], distr = 'gev', method = 'mle')

martindurocher/floodStat documentation built on May 31, 2019, 12:42 a.m.