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This function will generate n random points from an Inverse Gaussian distribution with a user provided, .mean, .shape, .dispersionThe function returns a tibble with the simulation number column the x column which corresponds to the n randomly generated points.

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_normal(
  .n = 50,
  .mean = 1,
  .shape = 1,
  .dispersion = 1/.shape,
  .num_sims = 1
)

Arguments

.n

The number of randomly generated points you want.

.mean

Must be strictly positive.

.shape

Must be strictly positive.

.dispersion

An alternative way to specify the .shape.

.num_sims

The number of randomly generated simulations you want.

Value

A tibble of randomly generated data.

Details

This function uses the underlying actuar::rinvgauss(). For more information please see rinvgauss()

Author

Steven P. Sanderson II, MPH

Examples

tidy_inverse_normal()
#> # A tibble: 50 × 7
#>    sim_number     x     y        dx      dy      p     q
#>    <fct>      <int> <dbl>     <dbl>   <dbl>  <dbl> <dbl>
#>  1 1              1 0.730 -0.468    0.00216 0.534  0.730
#>  2 1              2 1.01  -0.351    0.0111  0.670  1.01 
#>  3 1              3 0.689 -0.234    0.0426  0.509  0.689
#>  4 1              4 0.717 -0.117    0.125   0.526  0.717
#>  5 1              5 0.238 -0.000464 0.282   0.0999 0.238
#>  6 1              6 0.940  0.117    0.502   0.643  0.940
#>  7 1              7 1.52   0.233    0.722   0.814  1.52 
#>  8 1              8 0.420  0.350    0.870   0.290  0.420
#>  9 1              9 0.611  0.467    0.914   0.455  0.611
#> 10 1             10 1.29   0.584    0.861   0.764  1.29 
#> # ℹ 40 more rows