Description Usage Arguments Value Examples
compute_forall
computes the FAR evolution for all models according to the compute_far function taken as
an argument. if the argument p is provided, it also computes the
evolution of the corresponding quantile.
1 2 3 | compute_forall(compute_far, l_models = NULL, xp = 1.6, p = NULL,
y = "eur_tas", x = "gbl_tas", time = "year", ci_p = 0.9,
res_folder = deparse(substitute(compute_far)), ...)
|
compute_far |
a function used to compute the FAR |
l_models |
a character vectors giving the list of models for which the FAR needs to be computed. by default all models present in this packages are used |
xp |
the selected threshold used to define the FAR |
p |
the selected probability of exceedance to define the corresponding quantile |
y |
the name of the variable that will be used as y in the compute_far function |
x |
the name of the variable that will be used as the covariate x in the compute_far function |
time |
the name of the variable that will be used as the time variable in the compute_far function |
ci_p |
the level of the confidence intervals |
res_folder |
the folder where the results of compute_far for each model are saved as .rds. By default, creates a folder the name of which is the name of the function compute_far |
... |
additional parameters needed in the compute_far function if required |
return a dataframe with the confidence intervals for the FAR and all the other computed parameters (e.g, p and q) at each time
1 2 3 4 5 | # compute the FAR for the CNRM and the IPSL GCMs using a gam decomposition
# and a gaussian fit with only three bootstrap samples
ans <- compute_forall(compute_far.default, stat_model=gauss_fit, p=0.01, R=3)
# same with ebm decompositon and a gpd fit over the 90% quantil
ans <- compute_forall(compute_far.dx_ebm_fit, l_models=c("cnrm", "ipsl"), stat_model=gpd_fit, qthreshold =0.90, p=0.01, R=3)
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