Tidy Randomly Generated Burr Distribution Tibble — tidy_burr • TidyDensity
Description: 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.

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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
    )

## 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.

## 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_pareto1()`, `tidy_pareto()`, `tidy_t()`, `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              1 167.       -2.78  1.53e- 3 0.994    167.
    #>  2 1              2   0.575     0.752 2.26e- 1 0.365      0.575
    #>  3 1              3  35.8       4.28  4.79e- 2 0.973     35.8
    #>  4 1              4   0.000750  7.81  1.90e- 2 0.000749   0.000750
    #>  5 1              5   3.02     11.3   3.48e- 6 0.751      3.02
    #>  6 1              6   0.136    14.9   9.52e-16 0.120      0.136
    #>  7 1              7   7.57     18.4   8.42e-19 0.883      7.57
    #>  8 1              8 130.       21.9   0        0.992    130.
    #>  9 1              9   1.43     25.5   0        0.589      1.43
    #> 10 1             10   0.532    29.0   9.48e-14 0.347      0.532
    #> # ℹ 40 more rows