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# Tidy Randomly Generated Weibull Distribution Tibble

Source: `R/random-tidy-weibull.R`
`tidy_weibull.Rd`

This function will generate `n` random points from a weibull 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_weibull(
      .n = 50,
      .shape = 1,
      .scale = 1,
      .num_sims = 1,
      .return_tibble = TRUE
    )

## Arguments

.n

  The number of randomly generated points you want.

.shape

  Shape parameter defaults to 0.

.scale

  Scale parameter defaults to 1.

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

## See also

https://www.itl.nist.gov/div898/handbook/eda/section3/eda3669.htm

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_pareto1()`, `tidy_t()`, `tidy_triangular()`, `tidy_uniform()`, `tidy_zero_truncated_geometric()`

Other Weibull: `tidy_inverse_weibull()`, `util_weibull_param_estimate()`, `util_weibull_stats_tbl()`

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_weibull()
    #> # A tibble: 50 × 7
    #>    sim_number     x      y      dx      dy      p      q
    #>    <fct>      <int>  <dbl>   <dbl>   <dbl>  <dbl>  <dbl>
    #>  1 1              1 0.0463 -0.867  0.00100 0.0452 0.0463
    #>  2 1              2 1.91   -0.740  0.00367 0.852  1.91
    #>  3 1              3 0.595  -0.612  0.0114  0.448  0.595
    #>  4 1              4 1.17   -0.484  0.0302  0.688  1.17
    #>  5 1              5 0.722  -0.357  0.0683  0.514  0.722
    #>  6 1              6 0.854  -0.229  0.133   0.574  0.854
    #>  7 1              7 0.908  -0.101  0.226   0.597  0.908
    #>  8 1              8 2.93    0.0265 0.337   0.946  2.93
    #>  9 1              9 0.200   0.154  0.447   0.181  0.200
    #> 10 1             10 2.83    0.282  0.535   0.941  2.83
    #> # ℹ 40 more rows