Tidy Randomly Generated Inverse Gaussian Distribution Tibble — tidy_inverse_normal • TidyDensity
Description: 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.

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# Tidy Randomly Generated Inverse Gaussian Distribution Tibble

Source: `R/random-tidy-normal-inverse.R`
`tidy_inverse_normal.Rd`

This function will generate `n` random points from an Inverse Gaussian distribution with a user provided, `.mean`, `.shape`, `.dispersion`The 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()`

## See also

Other Continuous Distribution: `tidy_beta()`, `tidy_burr()`, `tidy_cauchy()`, `tidy_chisquare()`, `tidy_exponential()`, `tidy_f()`, `tidy_gamma()`, `tidy_generalized_beta()`, `tidy_generalized_pareto()`, `tidy_geometric()`, `tidy_inverse_burr()`, `tidy_inverse_exponential()`, `tidy_inverse_gamma()`, `tidy_inverse_pareto()`, `tidy_inverse_weibull()`, `tidy_logistic()`, `tidy_lognormal()`, `tidy_normal()`, `tidy_paralogistic()`, `tidy_pareto1()`, `tidy_pareto()`, `tidy_t()`, `tidy_uniform()`, `tidy_weibull()`, `tidy_zero_truncated_geometric()`

Other Gaussian: `tidy_normal()`, `util_normal_param_estimate()`, `util_normal_stats_tbl()`

Other Inverse Distribution: `tidy_inverse_burr()`, `tidy_inverse_exponential()`, `tidy_inverse_gamma()`, `tidy_inverse_pareto()`, `tidy_inverse_weibull()`

## 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