Skip to contents

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 7.85  -2.76    0.000909 0.887 7.85 
#>  2 1              2 0.242 -2.07    0.00764  0.195 0.242
#>  3 1              3 0.943 -1.38    0.0379   0.485 0.943
#>  4 1              4 3.44  -0.695   0.113    0.775 3.44 
#>  5 1              5 1.59  -0.00587 0.206    0.614 1.59 
#>  6 1              6 0.885  0.683   0.240    0.470 0.885
#>  7 1              7 0.644  1.37    0.194    0.392 0.644
#>  8 1              8 0.798  2.06    0.129    0.444 0.798
#>  9 1              9 2.38   2.75    0.0909   0.704 2.38 
#> 10 1             10 0.256  3.44    0.0754   0.204 0.256
#> # … with 40 more rows