knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(mobirep)
This package is associated with the article "Evaluating the efficacy of bivariate extreme modelling approaches for multi-hazard scenarios" https://nhess.copernicus.org/articles/20/2091/2020/nhess-20-2091-2020.html It includes 6 models, the joint tail KDE model derived from Cooley et al. (2019) https://doi.org/10.1007%2Fs10687-019-00348-0, the conditional extreme model initially developed by Heffernan and Tawn (2004) https://doi.org/10.1111/j.1467-9868.2004.02050.x and four copulae: Normal, Farlie-Gumbel-Morgenstern, Galambos and Gumbel.
* Import data example bivariate dataset of daily mean temperature (data from E-OBS; Cornes et al., 2018) and number of wildfire per day in Porto district (Portugal)
data(porto)
AnalogSel(fire01meantemp)
select extreme threshold for the two margins
tr1=0.9 tr2=0.9
remove NA form the dataset
fire01meantemp=na.omit(fire01meantemp) u=fire01meantemp
fit a GPD to both margins and create extrapolated time serie (useful to create level curves)
margins=Margins.mod(tr1,tr2,u=fire01meantemp)
jtres<-JT.KDE.ap(u2=u,pb=0.01,pobj=upobj,beta=100, vtau=vtau,kk=kk,devplot=F,mar1=uu[,1],mar2=uu[,2], px=pp[,1],py=pp[,2],interh=interh)
jt.dens<-kde(u,gridsize = 200) ltlo<-digit.curves.p(start=jtres$wq0ri[1,], as.matrix(wq0ri), nPoints=98, closed = FALSE) ltl<-densi.curv.em(jt.dens,ltlo, tl="l", ltl)
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