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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  1.65  -2.66 7.83e- 4 0.545  1.65 
#>  2 1              2  4.28   2.33 1.60e- 1 0.792  4.28 
#>  3 1              3  0.936  7.33 3.65e- 3 0.343  0.936
#>  4 1              4  1.11  12.3  7.25e- 3 0.405  1.11 
#>  5 1              5  1.25  17.3  1.88e- 4 0.450  1.25 
#>  6 1              6  2.05  22.3  2.21e-16 0.614  2.05 
#>  7 1              7  0.461 27.3  3.53e- 9 0.114  0.461
#>  8 1              8 32.6   32.3  1.07e- 2 0.970 32.6  
#>  9 1              9  0.877 37.3  1.22e- 5 0.320  0.877
#> 10 1             10  1.13  42.3  0        0.412  1.13 
#> # ℹ 40 more rows