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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 0.00178  -0.00975   0.178 0.162  0.00178 
#>  2 1              2 0.000509 -0.00841   0.607 0.0495 0.000509
#>  3 1              3 0.00490  -0.00708   1.76  0.380  0.00490 
#>  4 1              4 0.0166   -0.00574   4.42  0.784  0.0166  
#>  5 1              5 0.00866  -0.00441   9.51  0.564  0.00866 
#>  6 1              6 0.000515 -0.00307  17.8   0.0501 0.000515
#>  7 1              7 0.00812  -0.00174  28.9   0.542  0.00812 
#>  8 1              8 0.00300  -0.000400 41.4   0.256  0.00300 
#>  9 1              9 0.00110   0.000935 52.7   0.104  0.00110 
#> 10 1             10 0.00256   0.00227  60.5   0.224  0.00256 
#> # ℹ 40 more rows