See evmix::gpd
Usage
dist_genpareto(u = NULL, sigmau = NULL, xi = NULL)
dist_genpareto1(u = NULL, sigmau = NULL, xi = NULL)Arguments
- u
Scalar location parameter, or
NULLas a placeholder.- sigmau
Scalar scale parameter, or
NULLas a placeholder.- xi
Scalar shape parameter, or
NULLas a placeholder.
Details
All parameters can be overridden with
with_params = list(u = ..., sigmau = ..., xi = ...).
dist_genpareto1 is equivalent to dist_genpareto but enforces
bound constraints on xi to [0, 1].
This ensures unboundedness and finite expected value unless xi == 1.0.
See also
Other Distributions:
Distribution,
dist_bdegp(),
dist_beta(),
dist_binomial(),
dist_blended(),
dist_dirac(),
dist_discrete(),
dist_empirical(),
dist_erlangmix(),
dist_exponential(),
dist_gamma(),
dist_lognormal(),
dist_mixture(),
dist_negbinomial(),
dist_normal(),
dist_pareto(),
dist_poisson(),
dist_translate(),
dist_trunc(),
dist_uniform(),
dist_weibull()
Examples
d_genpareto <- dist_genpareto(u = 0, sigmau = 1, xi = 1)
x <- d_genpareto$sample(100)
d_emp <- dist_empirical(x)
d_genpareto$export_functions("gpd") # so fitdistrplus finds it
#> Exported `dgpd()`.
#> Exported `rgpd()`.
#> Exported `pgpd()`.
#> Exported `qgpd()`.
plot_distributions(
empirical = d_emp,
theoretical = d_genpareto,
estimated = d_genpareto,
with_params = list(
estimated = fit(dist_genpareto(), x)$params
),
.x = seq(0, 5, length.out = 100)
)
