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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,
  .return_tibble = TRUE
)

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.

.return_tibble

A logical value indicating whether to return the result as a tibble. Default is TRUE.

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 0.942 -1.65  0.000968 0.346 0.942
#>  2 1              2 0.910 -1.01  0.0153   0.333 0.910
#>  3 1              3 1.29  -0.378 0.0952   0.460 1.29 
#>  4 1              4 2.85   0.257 0.253    0.704 2.85 
#>  5 1              5 1.49   0.893 0.337    0.510 1.49 
#>  6 1              6 1.43   1.53  0.273    0.496 1.43 
#>  7 1              7 2.68   2.16  0.168    0.689 2.68 
#>  8 1              8 8.46   2.80  0.103    0.889 8.46 
#>  9 1              9 1.86   3.44  0.0550   0.585 1.86 
#> 10 1             10 1.68   4.07  0.0208   0.552 1.68 
#> # ℹ 40 more rows