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# Tidy Randomly Generated Burr Distribution Tibble

Source: `R/random-tidy-burr.R`
`tidy_burr.Rd`

This function will generate `n` random points from a Burr distribution with a user provided, `.shape1`, `.shape2`, `.scale`, `.rate`, 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_burr(
      .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 strictly positive.

.shape2

  Must be strictly positive.

.rate

  An alternative way to specify the `.scale`.

.scale

  Must be strictly 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::rburr()`, and its underlying `p`, `d`, and `q` functions. For more information please see `actuar::rburr()`

## See also

https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html

Other Continuous Distribution: `tidy_beta()`, `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_pareto1()`, `tidy_t()`, `tidy_triangular()`, `tidy_uniform()`, `tidy_weibull()`, `tidy_zero_truncated_geometric()`

Other Burr: `tidy_inverse_burr()`, `util_burr_param_estimate()`, `util_burr_stats_tbl()`

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_burr()
    #> # A tibble: 50 × 7
    #>    sim_number     x      y     dx       dy     p      q
    #>    <fct>      <int>  <dbl>  <dbl>    <dbl> <dbl>  <dbl>
    #>  1 1              1  0.371 -2.84  0.000966 0.271  0.371
    #>  2 1              2  5.25  -1.03  0.0666   0.840  5.25
    #>  3 1              3  7.27   0.790 0.237    0.879  7.27
    #>  4 1              4  1.72   2.61  0.104    0.633  1.72
    #>  5 1              5  0.857  4.42  0.0287   0.461  0.857
    #>  6 1              6  0.294  6.24  0.0255   0.227  0.294
    #>  7 1              7 12.5    8.05  0.0260   0.926 12.5
    #>  8 1              8  9.76   9.87  0.0171   0.907  9.76
    #>  9 1              9  0.874 11.7   0.0122   0.466  0.874
    #> 10 1             10  1.76  13.5   0.0105   0.638  1.76
    #> # ℹ 40 more rows