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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 0.296 -1.03   0.00135 0.161  0.296
#>  2 1              2 0.286 -0.882  0.00426 0.150  0.286
#>  3 1              3 0.336 -0.739  0.0118  0.204  0.336
#>  4 1              4 0.797 -0.595  0.0290  0.573  0.797
#>  5 1              5 1.01  -0.451  0.0623  0.671  1.01 
#>  6 1              6 2.09  -0.307  0.118   0.895  2.09 
#>  7 1              7 0.362 -0.164  0.199   0.231  0.362
#>  8 1              8 1.15  -0.0200 0.298   0.722  1.15 
#>  9 1              9 0.130  0.124  0.399   0.0145 0.130
#> 10 1             10 0.244  0.267  0.482   0.106  0.244
#> # ℹ 40 more rows