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This function will generate n random points from an inverse pareto distribution with a user provided, .shape, .scale, 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_pareto(
  .n = 50,
  .shape = 1,
  .scale = 1,
  .num_sims = 1,
  .return_tibble = TRUE
)

Arguments

.n

The number of randomly generated points you want.

.shape

Must be positive.

.scale

Must be 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::rinvpareto(), and its underlying p, d, and q functions. For more information please see actuar::rinvpareto()

Author

Steven P. Sanderson II, MPH

Examples

tidy_inverse_pareto()
#> # A tibble: 50 × 7
#>    sim_number     x      y     dx       dy      p      q
#>    <fct>      <int>  <dbl>  <dbl>    <dbl>  <dbl>  <dbl>
#>  1 1              1 0.0800 -2.11  0.000891 0.0741 0.0800
#>  2 1              2 2.21   -0.913 0.0532   0.689  2.21  
#>  3 1              3 2.45    0.282 0.289    0.710  2.45  
#>  4 1              4 3.23    1.48  0.212    0.763  3.23  
#>  5 1              5 0.683   2.67  0.0927   0.406  0.683 
#>  6 1              6 5.20    3.87  0.0564   0.839  5.20  
#>  7 1              7 7.44    5.07  0.0466   0.881  7.44  
#>  8 1              8 5.71    6.26  0.0221   0.851  5.71  
#>  9 1              9 0.377   7.46  0.0137   0.274  0.377 
#> 10 1             10 1.88    8.65  0.0133   0.653  1.88  
#> # ℹ 40 more rows