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This function will generate n random points from an inverse exponential distribution with a user provided, .rate or .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_exponential(.n = 50, .rate = 1, .scale = 1/.rate, .num_sims = 1)

Arguments

.n

The number of randomly generated points you want.

.rate

An alternative way to specify the .scale

.scale

Must be strictly 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::rinvexp(), and its underlying p, d, and q functions. For more information please see actuar::rinvexp()

Author

Steven P. Sanderson II, MPH

Examples

tidy_inverse_exponential()
#> # A tibble: 50 × 7
#>    sim_number     x     y     dx       dy      p     q
#>    <fct>      <int> <dbl>  <dbl>    <dbl>  <dbl> <dbl>
#>  1 1              1 1.31  -1.53  0.000836 0.466  1.31 
#>  2 1              2 1.33  -0.318 0.0776   0.472  1.33 
#>  3 1              3 1.01   0.895 0.332    0.373  1.01 
#>  4 1              4 2.16   2.11  0.184    0.629  2.16 
#>  5 1              5 0.479  3.32  0.0812   0.124  0.479
#>  6 1              6 4.81   4.53  0.0320   0.812  4.81 
#>  7 1              7 0.280  5.75  0.0177   0.0282 0.280
#>  8 1              8 0.866  6.96  0.0169   0.315  0.866
#>  9 1              9 0.786  8.17  0.0169   0.280  0.786
#> 10 1             10 1.38   9.39  0.0119   0.486  1.38 
#> # ℹ 40 more rows