SplineML: Spline ML fitting

In circularEV: Extreme Value Analysis for Circular Data

Spline ML fitting

Description

Spline ML fitting

Usage

SplineML(
  excesses,
  drc,
  thetaVec = 0:360,
  nBoot = 100,
  numIntKnots = 10,
  knotsType = "eqSpaced",
  lambda = seq(0, 2, by = 0.5),
  kappa = seq(0, 2, by = 0.5),
  nCandidatesInit = 1000,
  numCores = 2
)

Arguments

excesses	Excesses data
drc	Directional covariate
thetaVec	Grid values at which the threshold will be evaluated
nBoot	Number of bootstrap resamples
numIntKnots	Number of internal knots
knotsType	Position of knots. Default to "eqSpaced". Otherwise, the knots will be placed at the quantiles of observed directions.
lambda	Penalty parameter values for lambda
kappa	Penalty parameter values for kappa
nCandidatesInit	Number of initial parameter vectors. Optimisation will start with the best.
numCores	Number of CPU cores to be used

Details

See Konzen, E., Neves, C., and Jonathan, P. (2021). Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference. Environmetrics, 32(4), e2667.

Value

List of bootstrap estimates of shape and scale, and optimal values of lambda and kappa.

Examples

data(HsSP)
data(drc)
timeRange <- 54.5

idx <- order(drc)
drc <- drc[idx]
Data <- HsSP[idx]
set.seed(1234)
Data <- Data + runif(length(Data), -1e-4, 1e-4)

thetaVec <- 1:360

data(thresholdExampleML) # loads threshold example
thrResultML <- thresholdExampleML

lambda <- 100
kappa <- 40

thrPerObs <- thrResultML[drc]
excess <- Data - thrPerObs
drcExcess <- drc[excess>0]
excess <- excess[excess>0]



splineFit <- SplineML(excesses = excess, drc = drcExcess, nBoot = 30,
                      numIntKnots = 16, lambda=lambda, kappa=kappa, numCores=2)

xiBoot <- splineFit$xi
sigBoot <- splineFit$sig

PlotParamEstim(bootEstimates=xiBoot, thetaGrid=0:360, ylab=bquote(hat(xi)),
               alpha=0.05, ylim=NULL, cex.axis=15, cex.lab=2, thrWidth=2)

PlotParamEstim(bootEstimates=sigBoot, thetaGrid=0:360, ylab=bquote(hat(sigma)),
               alpha=0.05, ylim=NULL, cex.axis=15, cex.lab=2, thrWidth=2)

h <- 60 # needed for calculating local probability of exceedances
RLBoot <- CalcRLsplineML(Data=Data, drc=drc, xiBoot=xiBoot, sigBoot=sigBoot, h=h,
                         TTs=c(100, 10000), thetaGrid=thetaVec,
                         timeRange=timeRange, thr=thrResultML)

# 100-year level
PlotRL(RLBootList=RLBoot, thetaGrid=thetaVec, Data=Data, drc=drc,
       TTs=c(100, 10000), whichPlot=1, alpha=0.05, ylim=NULL,
       pointSize=1, cex.axis=15, cex.lab=2, thrWidth=2)

PolarPlotRL(RLBootList=RLBoot, thetaGrid=thetaVec, Data=Data, drc=drc,
            TTs=c(100, 10000), whichPlot=1, alpha=0.05, ylim=c(0, 25),
            pointSize=4, fontSize=12, lineWidth=2)



## See also examples in vignette:
# vignette("splineML", package = "circularEV")

circularEV documentation built on May 13, 2022, 5:08 p.m.