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# Tidy Randomly Generated Generalized Beta Distribution Tibble

Source: `R/random-tidy-general-beta.R`
`tidy_generalized_beta.Rd`

This function will generate `n` random points from a generalized beta distribution with a user provided, `.shape1`, `.shape2`, `.shape3`, `.rate`, and/or `.sclae`, 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_beta(
      .n = 50,
      .shape1 = 1,
      .shape2 = 1,
      .shape3 = 1,
      .rate = 1,
      .scale = 1/.rate,
      .num_sims = 1,
      .return_tibble = TRUE
    )

## Arguments

.n

  The number of randomly generated points you want.

.shape1

  A non-negative parameter of the Beta distribution.

.shape2

  A non-negative parameter of the Beta distribution.

.shape3

  A non-negative parameter of the Beta distribution.

.rate

  An alternative way to specify the `.scale` parameter.

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

## See also

https://statisticsglobe.com/beta-distribution-in-r-dbeta-pbeta-qbeta-rbeta

https://en.wikipedia.org/wiki/Beta_distribution

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_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 Beta: `tidy_beta()`, `util_beta_param_estimate()`, `util_beta_stats_tbl()`

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_generalized_beta()
    #> # A tibble: 50 × 7
    #>    sim_number     x      y      dx      dy      p      q
    #>    <fct>      <int>  <dbl>   <dbl>   <dbl>  <dbl>  <dbl>
    #>  1 1              1 0.781  -0.387  0.00215 0.781  0.781
    #>  2 1              2 0.713  -0.352  0.00501 0.713  0.713
    #>  3 1              3 0.193  -0.316  0.0109  0.193  0.193
    #>  4 1              4 0.0543 -0.280  0.0222  0.0543 0.0543
    #>  5 1              5 0.485  -0.244  0.0420  0.485  0.485
    #>  6 1              6 0.0240 -0.208  0.0743  0.0240 0.0240
    #>  7 1              7 0.204  -0.172  0.123   0.204  0.204
    #>  8 1              8 0.870  -0.136  0.191   0.870  0.870
    #>  9 1              9 0.463  -0.100  0.278   0.463  0.463
    #> 10 1             10 0.770  -0.0646 0.380   0.770  0.770
    #> # ℹ 40 more rows