RtsEva (version 1.1.0)

tsEvaPlotAllRLevelsGPD: tsEvaPlotAllRLevelsGPD

Description

tsEvaPlotAllRLevelsGPD is a function that generates a plot of return levels for a Generalized Pareto Distribution (GPD) based on the provided parameters and data. The plot showcases the evolving relationship between return periods and return levels, allowing for visual analysis of extreme events and their probabilities.

Usage

tsEvaPlotAllRLevelsGPD(
  nonStationaryEvaParams,
  stationaryTransformData,
  rlvmax,
  timeIndex,
  timeStamps,
  tstamps,
  trans,
  ...
)

Value

A plot object showing the relationship between return periods and return levels for the GPD distribution at different timest

Arguments

nonStationaryEvaParams

A list of non-stationary evaluation parameters containing the GPD distribution parameters (epsilon, sigma, threshold), time horizon start and end (thStart, thEnd), time horizon in years (timeHorizonInYears), and number of peaks (nPeaks).

stationaryTransformData

The stationary transformed data used for the analysis.

rlvmax

The maximum return level data, including the return periods (haz.RP) and the actual return levels (QNS).

timeIndex

The index of the time step used for analysis.

timeStamps

The timestamps corresponding to the time steps in the analysis.

tstamps

The timestamps used for labeling the plot.

trans

The transformation used to fit the EVD, either "ori" (original) or "rev" (reverse).

...

Additional optional arguments for customizing the plot.

See Also

tsEvaComputeReturnLevelsGPD

Examples

# Example usage of TsEvaNs function
timeAndSeries <- ArdecheStMartin
#go from six-hourly values to daily max
timeAndSeries <- max_daily_value(timeAndSeries)
#keep only the 20 last years
yrs <- as.integer(format(timeAndSeries$date, "%Y"))
tokeep <- which(yrs>=2000)
timeAndSeries <- timeAndSeries[tokeep,]
timeWindow <- 5*365 # 5 years
TSEVA_data <- TsEvaNs(timeAndSeries, timeWindow,
                      transfType = 'trendPeaks',tail = 'high')
nonStationaryEvaParams <- TSEVA_data[[1]]
stationaryTransformData <- TSEVA_data[[2]]
peax <- nonStationaryEvaParams[[2]]$parameters$peaks
peaxID <- nonStationaryEvaParams[[2]]$parameters$peakID
timeStamps <- stationaryTransformData$timeStamps
trendPeaks <- stationaryTransformData$trendSeries[peaxID]
stdPeaks <- stationaryTransformData$stdDevSeries[peaxID]
peaksCor <- (peax - trendPeaks) / stdPeaks
nYears <- round(length(timeStamps) / 365.25 )
rlvlmax <- empdis(peaksCor, nYears)
rlvlmax$QNS <- peax[order(peax)]
rlvlmax$Idt <- stationaryTransformData$timeStamps[peaxID][order(peax)]
timeIndex <- 2
tstamps <- "Example Timestamps"
trans <- "ori"
# Call the function with the defined arguments
result <- tsEvaPlotAllRLevelsGPD(
  nonStationaryEvaParams, stationaryTransformData,
  rlvlmax, timeIndex, timeStamps, tstamps,
  trans)
# Plot the result
result