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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()

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