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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  3.36  -1.62  0.000695 0.742   3.36 
#>  2 1              2  1.08  -1.19  0.00594  0.396   1.08 
#>  3 1              3  0.832 -0.759 0.0314   0.301   0.832
#>  4 1              4  4.43  -0.328 0.105    0.798   4.43 
#>  5 1              5  1.03   0.103 0.228    0.380   1.03 
#>  6 1              6  0.382  0.534 0.337    0.0728  0.382
#>  7 1              7 11.1    0.965 0.362    0.914  11.1  
#>  8 1              8  0.744  1.40  0.299    0.261   0.744
#>  9 1              9  3.85   1.83  0.200    0.771   3.85 
#> 10 1             10  1.37   2.26  0.123    0.482   1.37 
#> # … with 40 more rows