Skip to contents

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,
  .return_tibble = TRUE
)

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.

.return_tibble

A logical value indicating whether to return the result as a tibble. Default is TRUE.

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  2.27  -2.60  0.000536 0.643  2.27 
#>  2 1              2  1.73  -1.10  0.0321   0.561  1.73 
#>  3 1              3 68.0    0.394 0.204    0.985 68.0  
#>  4 1              4  1.13   1.89  0.190    0.413  1.13 
#>  5 1              5  2.76   3.39  0.0830   0.696  2.76 
#>  6 1              6  0.891  4.89  0.0453   0.325  0.891
#>  7 1              7 16.5    6.38  0.0163   0.941 16.5  
#>  8 1              8  2.61   7.88  0.00316  0.682  2.61 
#>  9 1              9  1.04   9.38  0.00539  0.381  1.04 
#> 10 1             10  1.32  10.9   0.00703  0.470  1.32 
#> # ℹ 40 more rows