FitPot: Peak over threshold (POT)
In martindurocher/floodStat: FloodNet Regional Frequency Analysis

Description Usage Arguments Details References See Also Examples

Fit the parameters of a model using peak over threshold (POT).

FitPot(x, ...)

## S3 method for class 'data.frame'
FitPot(obj, ...)

## S3 method for class 'matrix'
FitPot(obj, ...)

## S3 method for class 'formula'
FitPot(form, x, ...)

## S3 method for class 'numeric'
FitPot(x, dt = NULL, u = 0, method = "mle",
  declust = "none", r = 1, rlow = 0.75, nsim = 1000,
  varcov = TRUE)

## S3 method for class 'fpot'
coef(obj, rate = FALSE, ci = FALSE, alpha = 0.05)

## S3 method for class 'fpot'
vcov(obj, rate = FALSE)

## S3 method for class 'fpot'
print(obj)

## S3 method for class 'fpot'
predict(obj, q = c(0.5, 0.8, 0.9, 0.95, 0.98, 0.99),
  se = FALSE, ci = "none", alpha = 0.05, nsim = 1000, ...)

`x`	Sample.
`dt`	Date or time of observation.
`u`	Threshold.
`method`	Estimation method. Either `'lmom'`, `'mle'` or `'mle2'`.
`declust`	If necessary, declustering method. Either 'run' or 'wrc'.
`r`	Lag parameter for declustering. Either the running length betwee clusters or the minimum separating time between two flood Peaks. The scale must coincide with the observation date `dt`.
`rlow`	For WRC, recession level between two flood peaks in percentage.
`nsim`	Number of bootstrap samples.
`ci`	For 'coef' should confidence interval be returned. For 'predict', method used to compute confidence intervals. Either 'profile','delta' or 'boot'.
`se`	Should the standard error or the confidence interval be returned.

The access functions coef and vcov return respectively the parameters and the variance-covariance matrix of the POT model. For the L-moment method the covariance matrix is using bootstraps. The access function predict evaluates flood quantiles. If dt is a Date the return period is computed in year using the range of observation.

Coles S. (2001) An introduction to statistical modeling of extreme values. Springer Verlag.

Davison AC, Smith RL. (1990) Models for Exceedances over High Thresholds. Journal of the Royal Statistical Society Series B (Methodological). 52(3):393–442.

which.floodPeaks, which.clusters, PlotMrl.

xd <- rgpa(100, 1, -.2)
fit <- FitPot(xd, u = 0)

print(fit)
vcov(fit)
predict(fit)
coef(fit, ci = TRUE)

fit <- FitPot(flow~date, flowStJohn, u = 1000,
               declust = 'wrc', r = 14)

print(fit)
plot(flow~date,flowStJohn, type = 'l')
points(fit$time,fit$excess+fit$u, col = 2, pch = 16)
abline(h=1000, col = 3, lwd = 2)

predict(fit, se = TRUE, ci = 'delta')