Tidy Randomly Generated Inverse Weibull Distribution Tibble — tidy_inverse_weibull • TidyDensity
Description: This function will generate n random points from a weibull distribution with a user provided, .shape, .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.

Skip to contents

# Tidy Randomly Generated Inverse Weibull Distribution Tibble

Source: `R/random-tidy-weibull-inverse.R`
`tidy_inverse_weibull.Rd`

This function will generate `n` random points from a weibull distribution with a user provided, `.shape`, `.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_weibull(
      .n = 50,
      .shape = 1,
      .rate = 1,
      .scale = 1/.rate,
      .num_sims = 1
    )

## Arguments

.n

  The number of randomly generated points you want.

.shape

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

## 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_burr()`, `tidy_inverse_exponential()`, `tidy_inverse_gamma()`, `tidy_inverse_normal()`, `tidy_inverse_pareto()`, `tidy_logistic()`, `tidy_lognormal()`, `tidy_normal()`, `tidy_paralogistic()`, `tidy_pareto1()`, `tidy_pareto()`, `tidy_t()`, `tidy_uniform()`, `tidy_weibull()`, `tidy_zero_truncated_geometric()`

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

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

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_inverse_weibull()
    #> # A tibble: 50 × 7
    #>    sim_number     x      y     dx       dy      p      q
    #>    <fct>      <int>  <dbl>  <dbl>    <dbl>  <dbl>  <dbl>
    #>  1 1              1 26.2   -2.21  0.000574 0.963  26.2
    #>  2 1              2  1.95  -1.58  0.00559  0.598   1.95
    #>  3 1              3  3.40  -0.953 0.0311   0.745   3.40
    #>  4 1              4  0.432 -0.325 0.102    0.0986  0.432
    #>  5 1              5  1.15   0.303 0.205    0.420   1.15
    #>  6 1              6  4.88   0.931 0.268    0.815   4.88
    #>  7 1              7  1.19   1.56  0.246    0.433   1.19
    #>  8 1              8  0.780  2.19  0.174    0.277   0.780
    #>  9 1              9  0.929  2.81  0.105    0.341   0.929
    #> 10 1             10  2.47   3.44  0.0667   0.667   2.47
    #> # ℹ 40 more rows