Tidy Randomly Generated Inverse Burr Distribution Tibble — tidy_inverse_burr • TidyDensity
Description: This function will generate n random points from an Inverse 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 Inverse Burr Distribution Tibble

Source: `R/random-tidy-burr-inverse.R`
`tidy_inverse_burr.Rd`

This function will generate `n` random points from an Inverse 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_inverse_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::rinvburr()`, and its underlying `p`, `d`, and `q` functions. For more information please see `actuar::rinvburr()`

## 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_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_burr()`, `util_burr_param_estimate()`, `util_burr_stats_tbl()`

Other Inverse Distribution: `tidy_inverse_exponential()`, `tidy_inverse_gamma()`, `tidy_inverse_normal()`, `tidy_inverse_pareto()`, `tidy_inverse_weibull()`

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_inverse_burr()
    #> # A tibble: 50 × 7
    #>    sim_number     x      y     dx       dy      p      q
    #>    <fct>      <int>  <dbl>  <dbl>    <dbl>  <dbl>  <dbl>
    #>  1 1              1 0.494  -5.15  0.000716 0.331  0.494
    #>  2 1              2 1.91   -4.31  0.00294  0.657  1.91
    #>  3 1              3 7.74   -3.48  0.00965  0.886  7.74
    #>  4 1              4 3.75   -2.64  0.0253   0.790  3.75
    #>  5 1              5 4.84   -1.81  0.0532   0.829  4.84
    #>  6 1              6 0.0620 -0.974 0.0901   0.0584 0.0620
    #>  7 1              7 0.587  -0.139 0.124    0.370  0.587
    #>  8 1              8 1.52    0.696 0.140    0.603  1.52
    #>  9 1              9 0.267   1.53  0.133    0.211  0.267
    #> 10 1             10 6.21    2.37  0.108    0.861  6.21
    #> # ℹ 40 more rows