Cond.mod.ap: Fits the bivariate conditional extremes model
In mobirep: Models Bivariate Dependence and Produces Bivariate Return Periods

Description Usage Arguments Value References See Also Examples

Fits the bivariate conditional extremes model (from Heffernan and Tawn (2004) and texmex R package) and provides estimates of a conditional or joint exceedance level curve with a probability corresponding to 'pobj'. Also provides estimates of dependence measures.

Cond.mod.ap(
  u2,
  tr1,
  tr2,
  tsim,
  num.sim,
  pobj,
  interh = "comb",
  mar1,
  mar2,
  px,
  py
)

`u2`	Two column data frame
`tr1`	extreme threshold for first variable
`tr2`	extreme threshold for second variable
`tsim`	Prediction quantile. The quantile of the conditioning variable above which it will be simulated for importance sampling based prediction (from texmex)
`num.sim`	The number of simulated observations to be generated for prediction (from texmex)
`pobj`	objective joint return period modelled with the conditional extremes model
`interh`	type of hazard interrelation '`comb`' for compound (joint exceedance probability) and '`casc`' for cascade (conditional porbability)
`mar1`	Values of the first margin
`mar2`	Values of the second margin
`px`	Uniform values of the first margin with a mixed distribution (empirical below and gpd above a threshold)
`py`	Uniform values of the second margin with a mixed distribution (empirical below and gpd above a threshold)

a list containing the following:

jline - data frame of the objective level curve with the selected return period 'pobj'
onlysim - data frame of simulated extreme data for the two variables
etaHT - threshold dependent extremal dependence measure
chiHT - threshold dependent coefficient of tail dependence

Tilloy, A., Malamud, B.D., Winter, H. and Joly-Laugel, A., 2020. Evaluating the efficacy of bivariate extreme modelling approaches for multi-hazard scenarios. Natural Hazards and Earth System Sciences, 20(8), pp.2091-2117.

Heffernan, J.E. and Tawn, J.A., 2004. A conditional approach for multivariate extreme values (with discussion). Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66(3), pp.497-546.

mex

# Import data
data(porto)
tr1=0.9
tr2=0.9
fire01meantemp=na.omit(fire01meantemp)
u=fire01meantemp

#Compute uniform margins
marg=Margins.mod(tr1,tr2,u=fire01meantemp)
kk=marg$uvar
pp=marg$uvar_ext
uu=marg$val_ext

upobj=0.001
t.sim=0.98
interh="comb"
 ## Not run: 
# Fit conditional extremes model
condexres<-Cond.mod.ap(u2=u,tr1,tr2,tsim=t.sim,num.sim=10000,
pobj=upobj,mar1=uu[,1],mar2=uu[,2],px=pp[,1],py=pp[,2],interh=interh)

 plot(condexres$jline)
 
## End(Not run)