prof_p: Compute Confidence Intervals for the the probability of...

Description Usage Arguments Details Examples

View source: R/prof_far.R

Description

Compute Confidence Intervals for the probability of exceedance based on the profile likelihood methodology and constrained optimization.

Usage

1
prof_p(y_fit, xp, t, ci_p = 0.95, to_plot = FALSE, ...)

Arguments

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.

Details

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.

Examples

 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)

thaos/FARg documentation built on May 25, 2019, 8:18 a.m.