Package 'extremeIndex'

Function for heuristically choosing the domain where extreme value theory can be applied

Description

Function for heuristically choosing the domain where extreme value theory can be applied

Usage

choosethres(data, thresh, guess = c(1, 0.1), plots = 1:3, R = 200, ncpus = 1)

Arguments

data	a numeric vector containing the observation used for verification
thresh	vector of thresholds to try
guess	starting values for GPD's sigma and xi (0<xi<1)
plots	which parameter plots do you want
R	number of bootstrap estimates for confidence intervals
ncpus	if you want to make bootstrap on several cores

Value

three plots summarizing the stability of the parameters to threshold. The starting threshold admits kappa=1 and its confidence interval ; according Papastathopoulos & Tawn (2013)

a list with thresholds used, GP parameters and CIs, optimal threshold and xi.

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Observations of 6-h rainfall amount with CRPS values of 3 calibrated ensemble forecasts for one lead time across France.

Description

Observations of 6-h rainfall amount with CRPS values of 3 calibrated ensemble forecasts for one lead time across France.

Usage

crps

Format

A matrix with 112221 rows and 4 variables:

obs_rr6

observations, in mm/6h

crps_forecastX

CRPS values of the forecaster X, in mm/6h

...

Source

Maxime Taillardat

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Function which plots the index for differents forecasts sharing the same observations

Description

Function which plots the index for differents forecasts sharing the same observations

Usage

index.plot(forecasts, col = NULL, leg = NULL, xtypq = TRUE, ...)

Arguments

forecasts	list of "indexfore" objects, all forecasts must be computed on the same climatology and thresholds
col	colors of the differents forecasts for the plot
leg	legend of the plot
xtypq	the x-axis of the plot is quantiles values or orders (TRUE for quantiles)
...	other arguments for the plot

Value

a plot of the indices and a matrix containing the indexes for each threshold/order

Examples

data("crps")
y=crps[1:500,1]
cli=indexclim(y,thresh=seq(3,quantile(y,probs=0.995),length=2),xi=0.2)
frcst=crps[1:500,2]
idf=indexfore(frcst,cli)
frcst=crps[1:500,3]
idf2=indexfore(frcst,cli)
fore=list(idf,idf2)
idxp2=index.plot(fore,col=c("red","blue"),leg=c("forecast 1",
"forecast 2"),main="Index plot")

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Function which computes the index for the climatological CRPS/MAE. You must provide the observations. If you computes climatological CRPS/MAE previously, you can add the corresponding vector

Description

Function which computes the index for the climatological CRPS/MAE. You must provide the observations. If you computes climatological CRPS/MAE previously, you can add the corresponding vector

Usage

indexclim(
  y,
  thresh = NULL,
  score_clim = NULL,
  xi = NULL,
  score = "crps",
  estim_xi = FALSE
)

Arguments

y	The observations
thresh	Vector of thresholds where you want to compute the index
score_clim	If not NULL, must be the time serie of the CRPS/MAE of the climatology. It is recommended to compute CRPS/MAE out of this function
xi	Shape parameter of the GP ( xi > 0)
score	A character string indicating if you want to work with CRPS ("crps") or MAE ("mae"), by default "crps"
estim_xi	If you want xi estimated for each threshold (for numerical reasons for instance)

Value

An indexclim object containing xi, y, the score time serie, the score considered, the index values, and the corresponding quantiles of the observations

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Function for computing the index for a forecast system vs. climatological forecast. You must provide an indexclim object.

Description

Function for computing the index for a forecast system vs. climatological forecast. You must provide an indexclim object.

Usage

indexfore(score_fore, clim)

Arguments

score_fore	the time serie of the ensemble forecast's CRPS/MAE. Be careful that score_fore is consistent with "score" in indexclim
clim	an indexclim object coming from indexclim

Value

an indexfore object with the index computed vs. climatological forecast and the statistic omega2

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