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# Tidy Randomly Generated Pareto Distribution Tibble

Source: `R/random-tidy-pareto.R`
`tidy_pareto.Rd`

This function will generate `n` random points from a pareto distribution with a user provided, `.shape`, `.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_pareto(
      .n = 50,
      .shape = 10,
      .scale = 0.1,
      .num_sims = 1,
      .return_tibble = TRUE
    )

## Arguments

.n

  The number of randomly generated points you want.

.shape

  Must be positive.

.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::rpareto()`, and its underlying `p`, `d`, and `q` functions. For more information please see `actuar::rpareto()`

## 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_generalized_pareto()`, `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_t()`, `tidy_triangular()`, `tidy_uniform()`, `tidy_weibull()`, `tidy_zero_truncated_geometric()`

Other Pareto: `tidy_generalized_pareto()`, `tidy_inverse_pareto()`, `tidy_pareto1()`, `util_pareto_param_estimate()`, `util_pareto_stats_tbl()`

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_pareto()
    #> # A tibble: 50 × 7
    #>    sim_number     x        y        dx     dy      p        q
    #>    <fct>      <int>    <dbl>     <dbl>  <dbl>  <dbl>    <dbl>
    #>  1 1              1 0.00866  -0.0112    0.157 0.564  0.00866
    #>  2 1              2 0.0148   -0.00984   0.452 0.749  0.0148
    #>  3 1              3 0.000112 -0.00851   1.16  0.0111 0.000112
    #>  4 1              4 0.00653  -0.00718   2.64  0.469  0.00653
    #>  5 1              5 0.00270  -0.00585   5.36  0.234  0.00270
    #>  6 1              6 0.0158   -0.00453   9.74  0.769  0.0158
    #>  7 1              7 0.00248  -0.00320  15.9   0.218  0.00248
    #>  8 1              8 0.0112   -0.00187  23.4   0.654  0.0112
    #>  9 1              9 0.0149   -0.000541 31.2   0.752  0.0149
    #> 10 1             10 0.0183    0.000787 38.0   0.814  0.0183
    #> # ℹ 40 more rows