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# 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 of `n` for the current simulation.

- `y` The randomly generated data point.

- `dx` The `x` value from the `stats::density()` function.

- `dy` The `y` value from the `stats::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.

## Value

A tibble of randomly generated data.

## 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_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_pareto_param_estimate()`, `util_pareto_stats_tbl()`

## Author

Steven P. Sanderson II, MPH

## 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 0.731   -1.35 3.20e- 1 0.422  0.731
    #>  2 1              2 1.12    36.3  9.01e-17 0.529  1.12
    #>  3 1              3 1.27    74.0  0        0.559  1.27
    #>  4 1              4 0.881  112.   9.27e-19 0.468  0.881
    #>  5 1              5 2.06   149.   0        0.674  2.06
    #>  6 1              6 0.0461 187.   1.34e-19 0.0440 0.0461
    #>  7 1              7 2.83   225.   5.64e-18 0.739  2.83
    #>  8 1              8 1.82   262.   0        0.645  1.82
    #>  9 1              9 1.41   300.   1.41e-18 0.585  1.41
    #> 10 1             10 1.42   338.   9.29e-19 0.586  1.42
    #> # ℹ 40 more rows