Tidy Randomly Generated Pareto Distribution Tibble — tidy_pareto • TidyDensity
Description: This function will generate n random points from a 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 Pareto Distribution Tibble

Source: `R/random-tidy-pareto.R`
`tidy_pareto.Rd`

This function will generate `n` random points from a 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_pareto(.n = 50, .shape = 10, .scale = 0.1, .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::rpareto()`, and its underlying `p`, `d`, and `q` functions. For more information please see `actuar::rpareto()`

## 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_pareto()`, `tidy_inverse_weibull()`, `tidy_logistic()`, `tidy_lognormal()`, `tidy_normal()`, `tidy_paralogistic()`, `tidy_pareto1()`, `tidy_t()`, `tidy_uniform()`, `tidy_weibull()`, `tidy_zero_truncated_geometric()`

Other Pareto: `tidy_generalized_pareto()`, `tidy_inverse_pareto()`, `tidy_pareto1()`, `util_pareto_param_estimate()`, `util_pareto_stats_tbl()`

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_pareto()
    #> # A tibble: 50 × 7
    #>    sim_number     x        y        dx     dy      p        q
    #>    <fct>      <int>    <dbl>     <dbl>  <dbl>  <dbl>    <dbl>
    #>  1 1              1 0.000429 -0.0110    0.133 0.0419 0.000429
    #>  2 1              2 0.000701 -0.00954   0.433 0.0675 0.000701
    #>  3 1              3 0.0164   -0.00808   1.22  0.781  0.0164
    #>  4 1              4 0.00652  -0.00662   3.01  0.468  0.00652
    #>  5 1              5 0.00513  -0.00516   6.46  0.393  0.00513
    #>  6 1              6 0.0104   -0.00370  12.1   0.629  0.0104
    #>  7 1              7 0.00402  -0.00225  20.1   0.326  0.00402
    #>  8 1              8 0.0343   -0.000787 29.5   0.947  0.0343
    #>  9 1              9 0.00562   0.000672 38.7   0.421  0.00562
    #> 10 1             10 0.000117  0.00213  45.7   0.0116 0.000117
    #> # ℹ 40 more rows