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This function will generate n random points from a 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_pareto(.n = 50, .shape = 10, .scale = 0.1, .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::rpareto(), and its underlying p, d, and q functions. For more information please see actuar::rpareto()

Author

Steven P. Sanderson II, MPH

Examples

tidy_pareto()
#> # A tibble: 50 × 7
#>    sim_number     x        y        dx     dy      p        q
#>    <fct>      <int>    <dbl>     <dbl>  <dbl>  <dbl>    <dbl>
#>  1 1              1 0.000429 -0.0110    0.133 0.0419 0.000429
#>  2 1              2 0.000701 -0.00954   0.433 0.0675 0.000701
#>  3 1              3 0.0164   -0.00808   1.22  0.781  0.0164  
#>  4 1              4 0.00652  -0.00662   3.01  0.468  0.00652 
#>  5 1              5 0.00513  -0.00516   6.46  0.393  0.00513 
#>  6 1              6 0.0104   -0.00370  12.1   0.629  0.0104  
#>  7 1              7 0.00402  -0.00225  20.1   0.326  0.00402 
#>  8 1              8 0.0343   -0.000787 29.5   0.947  0.0343  
#>  9 1              9 0.00562   0.000672 38.7   0.421  0.00562 
#> 10 1             10 0.000117  0.00213  45.7   0.0116 0.000117
#> # ℹ 40 more rows