
Tidy Randomly Generated Generalized Pareto Distribution Tibble
Source:R/random-tidy-general-pareto.R
tidy_generalized_pareto.Rd
This function will generate n
random points from a generalized
Pareto distribution with a user provided, .shape1
, .shape2
, .rate
or
.scale
and number of #' random simulations to be produced.
The function returns a tibble with the simulation number column the x column
which corresponds to the n randomly generated points, the d_
, p_
and q_
data points as well.
The data is returned un-grouped.
The columns that are output are:
sim_number
The current simulation number.x
The current value ofn
for the current simulation.y
The randomly generated data point.dx
Thex
value from thestats::density()
function.dy
They
value from thestats::density()
function.p
The values from the resulting p_ function of the distribution family.q
The values from the resulting q_ function of the distribution family.
Usage
tidy_generalized_pareto(
.n = 50,
.shape1 = 1,
.shape2 = 1,
.rate = 1,
.scale = 1/.rate,
.num_sims = 1,
.return_tibble = TRUE
)
Arguments
- .n
The number of randomly generated points you want.
- .shape1
Must be positive.
- .shape2
Must be positive.
- .rate
An alternative way to specify the
.scale
argument- .scale
Must be positive.
- .num_sims
The number of randomly generated simulations you want.
- .return_tibble
A logical value indicating whether to return the result as a tibble. Default is TRUE.
Details
This function uses the underlying actuar::rgenpareto()
, and its underlying
p
, d
, and q
functions. For more information please see actuar::rgenpareto()
See also
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Continuous Distribution:
tidy_beta()
,
tidy_burr()
,
tidy_cauchy()
,
tidy_chisquare()
,
tidy_exponential()
,
tidy_f()
,
tidy_gamma()
,
tidy_generalized_beta()
,
tidy_inverse_burr()
,
tidy_inverse_exponential()
,
tidy_inverse_gamma()
,
tidy_inverse_normal()
,
tidy_inverse_pareto()
,
tidy_inverse_weibull()
,
tidy_logistic()
,
tidy_lognormal()
,
tidy_normal()
,
tidy_paralogistic()
,
tidy_pareto()
,
tidy_pareto1()
,
tidy_t()
,
tidy_triangular()
,
tidy_uniform()
,
tidy_weibull()
,
tidy_zero_truncated_geometric()
Other Pareto:
tidy_inverse_pareto()
,
tidy_pareto()
,
tidy_pareto1()
,
util_pareto1_aic()
,
util_pareto1_param_estimate()
,
util_pareto1_stats_tbl()
,
util_pareto_param_estimate()
,
util_pareto_stats_tbl()
Examples
tidy_generalized_pareto()
#> # A tibble: 50 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 0.348 -2.07 2.12e- 3 0.258 0.348
#> 2 1 2 1.15 3.71 3.82e- 2 0.536 1.15
#> 3 1 3 0.281 9.50 2.24e- 3 0.219 0.281
#> 4 1 4 2.71 15.3 4.41e- 3 0.730 2.71
#> 5 1 5 59.4 21.1 2.39e- 4 0.983 59.4
#> 6 1 6 0.965 26.9 1.03e-17 0.491 0.965
#> 7 1 7 1.85 32.6 7.52e-18 0.649 1.85
#> 8 1 8 0.971 38.4 0 0.493 0.971
#> 9 1 9 2.74 44.2 3.81e-18 0.733 2.74
#> 10 1 10 1.11 50.0 0 0.527 1.11
#> # ℹ 40 more rows