ThrSelection {circularEV} | R Documentation |
Threshold selection
Description
This function selects a moving threshold for circular data using an automatic procedure for selecting the local number of exceedances
Usage
ThrSelection(
Data,
drc,
h = 30,
b = 0.35,
thetaGrid,
EVIestimator = "ML",
useKernel = TRUE,
concent = 10,
bw = 30,
numCores = 2
)
Arguments
Data |
Response variable |
drc |
Directional covariate |
h |
Bandwidth value |
b |
Parameter used in the automatic procedure for selection of local number of exceedances |
thetaGrid |
Grid values at which the estimation is performed |
EVIestimator |
It can be either "ML" or "Mom" |
useKernel |
Logical. If TRUE (default), use kernel to assign weights depending on the directional distance. |
concent |
Concentration parameter value for von Mises kernel |
bw |
Bandwidth parameter value for smoothing the sample path of the selected threshold |
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 containing the selected threshold and selected number of local exceedances at each direction in the grid.
See Also
PlotData
and PolarPlotData
to see how
the threshold can be visualised.
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
thrResultMom <- ThrSelection(Data=Data, drc=drc, h=60, b=0.35, thetaGrid=thetaVec,
EVIestimator="Mom", useKernel=T, concent=10, bw=30,
numCores=2)$thr
thrResultML <- ThrSelection(Data=Data, drc=drc, h=60, b=0.35, thetaGrid=thetaVec,
EVIestimator="ML", useKernel=T, concent=10, bw=30,
numCores=2)$thr
## See also examples in vignettes:
# vignette("localMethods", package = "circularEV")
# vignette("splineML", package = "circularEV")