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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   1.60  -3.84 1.07e- 3 0.535     1.60 
#>  2 1              2 541.     7.36 3.04e- 2 0.998   541.   
#>  3 1              3  96.8   18.6  4.70e- 3 0.990    96.8  
#>  4 1              4  17.4   29.8  1.33e- 9 0.944    17.4  
#>  5 1              5   2.80  41.0  0        0.700     2.80 
#>  6 1              6   0.186 52.2  1.21e- 9 0.00467   0.186
#>  7 1              7   1.58  63.4  6.75e- 4 0.530     1.58 
#>  8 1              8   2.38  74.6  0        0.657     2.38 
#>  9 1              9   0.750 85.8  1.96e-15 0.264     0.750
#> 10 1             10   4.42  97.0  5.54e- 3 0.798     4.42 
#> # ℹ 40 more rows