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# Tidy Randomly Generated Pareto Single Parameter Distribution Tibble

Source: `R/random-tidy-pareto-single-param.R`
`tidy_pareto1.Rd`

This function will generate `n` random points from a single parameter pareto distribution with a user provided, `.shape`, `.min`, 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_pareto1(
      .n = 50,
      .shape = 1,
      .min = 1,
      .num_sims = 1,
      .return_tibble = TRUE
    )

## Arguments

.n

  The number of randomly generated points you want.

.shape

  Must be positive.

.min

  The lower bound of the support of the distribution.

.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::rpareto1()`, and its underlying `p`, `d`, and `q` functions. For more information please see `actuar::rpareto1()`

## 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_pareto()`, `tidy_t()`, `tidy_triangular()`, `tidy_uniform()`, `tidy_weibull()`, `tidy_zero_truncated_geometric()`

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

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_pareto1()
    #> # A tibble: 50 × 7
    #>    sim_number     x     y      dx       dy      p     q
    #>    <fct>      <int> <dbl>   <dbl>    <dbl>  <dbl> <dbl>
    #>  1 1              1  1.47 -2.65   0.000922 0.321   1.47
    #>  2 1              2  1.05 -1.32   0.0156   0.0485  1.05
    #>  3 1              3  8.45  0.0124 0.0886   0.882   8.45
    #>  4 1              4  9.99  1.35   0.179    0.900   9.99
    #>  5 1              5 45.3   2.68   0.149    0.978  45.3
    #>  6 1              6  1.45  4.01   0.0776   0.312   1.45
    #>  7 1              7  2.51  5.34   0.0499   0.602   2.51
    #>  8 1              8  1.51  6.68   0.0305   0.338   1.51
    #>  9 1              9  7.32  8.01   0.0200   0.863   7.32
    #> 10 1             10  2.12  9.34   0.0200   0.529   2.12
    #> # ℹ 40 more rows