Tidy Randomly Generated Inverse Pareto Distribution Tibble — tidy_inverse_pareto • TidyDensity
Description: This function will generate n random points from an inverse pareto distribution with a user provided, .shape, .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 Pareto Distribution Tibble

Source: `R/random-tidy-pareto-inverse.R`
`tidy_inverse_pareto.Rd`

This function will generate `n` random points from an inverse pareto distribution with a user provided, `.shape`, `.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_pareto(.n = 50, .shape = 1, .scale = 1, .num_sims = 1)

## Arguments

.n

  The number of randomly generated points you want.

.shape

  Must be positive.

.scale

  Must be 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::rinvpareto()`, and its underlying `p`, `d`, and `q` functions. For more information please see `actuar::rinvpareto()`

## 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_exponential()`, `tidy_inverse_gamma()`, `tidy_inverse_normal()`, `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 Pareto: `tidy_generalized_pareto()`, `tidy_pareto1()`, `tidy_pareto()`, `util_pareto_param_estimate()`, `util_pareto_stats_tbl()`

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

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_inverse_pareto()
    #> # A tibble: 50 × 7
    #>    sim_number     x      y     dx      dy      p      q
    #>    <fct>      <int>  <dbl>  <dbl>   <dbl>  <dbl>  <dbl>
    #>  1 1              1 0.477  -1.74  0.00124 0.323  0.477
    #>  2 1              2 0.0281 -1.03  0.0306  0.0273 0.0281
    #>  3 1              3 0.101  -0.310 0.191   0.0914 0.101
    #>  4 1              4 2.21    0.405 0.348   0.689  2.21
    #>  5 1              5 1.88    1.12  0.266   0.653  1.88
    #>  6 1              6 0.622   1.84  0.167   0.383  0.622
    #>  7 1              7 1.61    2.55  0.0899  0.617  1.61
    #>  8 1              8 0.445   3.27  0.0506  0.308  0.445
    #>  9 1              9 0.578   3.98  0.0364  0.366  0.578
    #> 10 1             10 0.787   4.70  0.0284  0.441  0.787
    #> # ℹ 40 more rows