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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 0.340 -0.300  0.00273 0.208  0.340
#>  2 1              2 0.577 -0.187  0.0220  0.429  0.577
#>  3 1              3 0.165 -0.0734 0.109   0.0352 0.165
#>  4 1              4 0.961  0.0399 0.345   0.652  0.961
#>  5 1              5 0.614  0.153  0.716   0.457  0.614
#>  6 1              6 0.635  0.266  1.04    0.472  0.635
#>  7 1              7 0.560  0.380  1.15    0.416  0.560
#>  8 1              8 1.09   0.493  1.09    0.701  1.09 
#>  9 1              9 4.17   0.606  0.924   0.982  4.17 
#> 10 1             10 4.79   0.720  0.724   0.988  4.79 
#> # ℹ 40 more rows