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This function will generate n random points from a single parameter pareto distribution with a user provided, .shape, .min, 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_pareto1(.n = 50, .shape = 1, .min = 1, .num_sims = 1)

Arguments

.n

The number of randomly generated points you want.

.shape

Must be positive.

.min

The lower bound of the support of the distribution.

.num_sims

The number of randomly generated simulations you want.

Value

A tibble of randomly generated data.

Details

This function uses the underlying actuar::rpareto1(), and its underlying p, d, and q functions. For more information please see actuar::rpareto1()

Author

Steven P. Sanderson II, MPH

Examples

tidy_pareto1()
#> # A tibble: 50 × 7
#>    sim_number     x     y      dx      dy      p     q
#>    <fct>      <int> <dbl>   <dbl>   <dbl>  <dbl> <dbl>
#>  1 1              1  3.69 -1.19   0.00133 0.729   3.69
#>  2 1              2  1.57 -0.772  0.00694 0.361   1.57
#>  3 1              3  1.06 -0.350  0.0264  0.0593  1.06
#>  4 1              4  1.27  0.0717 0.0741  0.211   1.27
#>  5 1              5  4.02  0.494  0.155   0.751   4.02
#>  6 1              6  1.80  0.916  0.246   0.445   1.80
#>  7 1              7  1.06  1.34   0.303   0.0528  1.06
#>  8 1              8  1.11  1.76   0.299   0.100   1.11
#>  9 1              9 10.9   2.18   0.247   0.908  10.9 
#> 10 1             10  1.02  2.60   0.183   0.0152  1.02
#> # ℹ 40 more rows