Description Usage Arguments References Examples
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.
1 2 |
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 |
... |
Other parameter pass to |
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | 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')
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