
Tidy Randomly Generated Generalized Pareto Distribution Tibble
Source:R/random-tidy-general-pareto.R
tidy_generalized_pareto.RdThis 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_numberThe current simulation number.xThe current value ofnfor the current simulation.yThe randomly generated data point.dxThexvalue from thestats::density()function.dyTheyvalue from thestats::density()function.pThe values from the resulting p_ function of the distribution family.qThe 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
)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
.scaleargument- .scale
Must be positive.
- .num_sims
The number of randomly generated simulations you want.
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_geometric(),
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_pareto1(),
tidy_pareto(),
tidy_t(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Pareto:
tidy_inverse_pareto(),
tidy_pareto1(),
tidy_pareto(),
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 0.773 -1.61 0.00119 0.436 0.773
#> 2 1 2 2.10 -0.908 0.0337 0.677 2.10
#> 3 1 3 1.66 -0.205 0.212 0.624 1.66
#> 4 1 4 0.390 0.499 0.371 0.281 0.390
#> 5 1 5 1.52 1.20 0.284 0.603 1.52
#> 6 1 6 2.54 1.91 0.148 0.717 2.54
#> 7 1 7 4.55 2.61 0.0579 0.820 4.55
#> 8 1 8 0.844 3.31 0.0390 0.458 0.844
#> 9 1 9 4.01 4.02 0.0677 0.801 4.01
#> 10 1 10 0.0134 4.72 0.0544 0.0132 0.0134
#> # ℹ 40 more rows