Tidy Randomly Generated Generalized Pareto Distribution Tibble — tidy_generalized_pareto • TidyDensity
Description: 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.

Skip to contents

# 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
    )

## 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.

## 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_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()`

## 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  3.94 -3.49  0.000760 0.798  3.94
    #>  2 1              2 92.6  -1.46  0.0427   0.989 92.6
    #>  3 1              3  2.42  0.573 0.163    0.708  2.42
    #>  4 1              4  2.80  2.60  0.123    0.737  2.80
    #>  5 1              5  3.25  4.64  0.0601   0.764  3.25
    #>  6 1              6  1.22  6.67  0.00905  0.549  1.22
    #>  7 1              7  1.65  8.70  0.0332   0.623  1.65
    #>  8 1              8 21.4  10.7   0.0201   0.955 21.4
    #>  9 1              9 44.4  12.8   0.00293  0.978 44.4
    #> 10 1             10  2.56 14.8   0.00669  0.719  2.56
    #> # ℹ 40 more rows