Description Usage Arguments Details Examples
Compute Confidence Intervals for the probability of exceedance based on the profile likelihood methodology and constrained optimization.
1 |
y_fit |
an object of class gauss_fit, gev_fit or gpd_fit. |
xp |
the threshold used to compute the probability of exeeding that threshold. |
t |
the time t to compute the probability of exeeding xp. If the time t is not present in dataset used for fitting the model, the closes time of the dataset is used. |
ci_p |
the confidence level of the confidence intervals (between 0 and 1). |
to_plot |
if TRUE, the function plots the offset profile likelihood. The profile likelihood plot is made in such a way that the FAR value is considered within the confidences intervals if the offset likelihood is above 0. |
... |
Arguments to be passed to methods. |
The profile likelihood for the probability of exceedance is computed using constrained optimization which allow us to minimize the negative likelihood for a given value of the probability. The method for the optimization was the Augmented Lagrangian and Adaptive Barrier Minimization Algorithm implemeted in the auglag
function of the alabama package.
1 2 3 4 5 6 7 8 9 10 11 12 13 | data(tas)
ge_fit <- gev_fit(eur_tas, data=tas, mu_mod=~gbl_tas, sig_mod=~gbl_tas, time_var="year")
gp_fit <- gpd_fit(eur_tas, data=tas, mu_mod=~gbl_tas, sig_mod=~gbl_tas, time_var="year", qthreshold=0.9)
ga_fit <- gauss_fit(eur_tas, data=tas, mu_mod=~gbl_tas, sig_mod=~gbl_tas, time_var="year")
t1 <- 2003
t0 <- 1990
xp <- 1.6
p_gpd <- prof_p(gp_fit, xp, t0, ci_p=0.95, to_plot=TRUE)
p_gev <- prof_p(ge_fit, xp, t0, ci_p=0.95, to_plot=TRUE)
p_gauss <- prof_p(ga_fit, xp, t0, ci_p=0.95, to_plot=TRUE)
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