Tidy Randomly Generated Inverse Exponential Distribution Tibble — tidy_inverse_exponential • TidyDensity
Description: 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.

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# Tidy Randomly Generated Inverse Exponential Distribution Tibble

Source: `R/random-tidy-exponential-inverse.R`
`tidy_inverse_exponential.Rd`

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()`

## See also

https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html

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_gamma()`, `tidy_inverse_normal()`, `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 Exponential: `tidy_exponential()`, `util_exponential_param_estimate()`, `util_exponential_stats_tbl()`

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

## 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 3.60  -2.03  0.000608 0.757   3.60
    #>  2 1              2 0.203 -1.12  0.0150   0.00720 0.203
    #>  3 1              3 2.17  -0.220 0.0986   0.630   2.17
    #>  4 1              4 1.38   0.683 0.221    0.484   1.38
    #>  5 1              5 1.47   1.59  0.233    0.508   1.47
    #>  6 1              6 3.48   2.49  0.164    0.750   3.48
    #>  7 1              7 1.10   3.39  0.140    0.402   1.10
    #>  8 1              8 3.58   4.30  0.0994   0.756   3.58
    #>  9 1              9 3.33   5.20  0.0424   0.740   3.33
    #> 10 1             10 3.44   6.11  0.0318   0.748   3.44
    #> # ℹ 40 more rows