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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 0.477  -1.74  0.00124 0.323  0.477 
#>  2 1              2 0.0281 -1.03  0.0306  0.0273 0.0281
#>  3 1              3 0.101  -0.310 0.191   0.0914 0.101 
#>  4 1              4 2.21    0.405 0.348   0.689  2.21  
#>  5 1              5 1.88    1.12  0.266   0.653  1.88  
#>  6 1              6 0.622   1.84  0.167   0.383  0.622 
#>  7 1              7 1.61    2.55  0.0899  0.617  1.61  
#>  8 1              8 0.445   3.27  0.0506  0.308  0.445 
#>  9 1              9 0.578   3.98  0.0364  0.366  0.578 
#> 10 1             10 0.787   4.70  0.0284  0.441  0.787 
#> # ℹ 40 more rows