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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)

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.

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  7.27   -1.88  0.00109 0.879    7.27  
#>  2 1              2  0.0503 -1.09  0.0291  0.0479   0.0503
#>  3 1              3  0.0101 -0.287 0.185   0.00996  0.0101
#>  4 1              4  0.742   0.512 0.324   0.426    0.742 
#>  5 1              5 12.5     1.31  0.234   0.926   12.5   
#>  6 1              6  0.268   2.11  0.142   0.211    0.268 
#>  7 1              7  1.84    2.91  0.0634  0.648    1.84  
#>  8 1              8  2.39    3.71  0.0193  0.705    2.39  
#>  9 1              9  0.157   4.51  0.00378 0.136    0.157 
#> 10 1             10 21.6     5.31  0.00497 0.956   21.6   
#> # ℹ 40 more rows