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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  2.67 -1.28  0.00109 0.625   2.67
#>  2 1              2  2.02 -0.805 0.00640 0.504   2.02
#>  3 1              3  1.20 -0.331 0.0264  0.163   1.20
#>  4 1              4  5.46  0.142 0.0775  0.817   5.46
#>  5 1              5  2.37  0.616 0.163   0.577   2.37
#>  6 1              6  1.68  1.09  0.252   0.406   1.68
#>  7 1              7  2.58  1.56  0.295   0.613   2.58
#>  8 1              8  1.30  2.04  0.273   0.228   1.30
#>  9 1              9 19.6   2.51  0.213   0.949  19.6 
#> 10 1             10  1.03  2.98  0.152   0.0331  1.03
#> # … with 40 more rows