Tidy Randomly Generated Generalized Beta Distribution Tibble — tidy_generalized_beta • TidyDensity
Description: This function will generate n random points from a generalized beta distribution with a user provided, .shape1, .shape2, .shape3, .rate, and/or .sclae, 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 Generalized Beta Distribution Tibble

Source: `R/random-tidy-general-beta.R`
`tidy_generalized_beta.Rd`

This function will generate `n` random points from a generalized beta distribution with a user provided, `.shape1`, `.shape2`, `.shape3`, `.rate`, and/or `.sclae`, 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_generalized_beta(
      .n = 50,
      .shape1 = 1,
      .shape2 = 1,
      .shape3 = 1,
      .rate = 1,
      .scale = 1/.rate,
      .num_sims = 1
    )

## Arguments

.n

  The number of randomly generated points you want.

.shape1

  A non-negative parameter of the Beta distribution.

.shape2

  A non-negative parameter of the Beta distribution.

.shape3

  A non-negative parameter of the Beta distribution.

.rate

  An alternative way to specify the `.scale` parameter.

.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 `stats::rbeta()`, and its underlying `p`, `d`, and `q` functions. For more information please see `stats::rbeta()`

## See also

https://statisticsglobe.com/beta-distribution-in-r-dbeta-pbeta-qbeta-rbeta

https://en.wikipedia.org/wiki/Beta_distribution

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_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_pareto()`, `tidy_t()`, `tidy_uniform()`, `tidy_weibull()`, `tidy_zero_truncated_geometric()`

Other Beta: `tidy_beta()`, `util_beta_param_estimate()`, `util_beta_stats_tbl()`

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_generalized_beta()
    #> # A tibble: 50 × 7
    #>    sim_number     x     y      dx      dy     p     q
    #>    <fct>      <int> <dbl>   <dbl>   <dbl> <dbl> <dbl>
    #>  1 1              1 0.889 -0.361  0.00254 0.889 0.889
    #>  2 1              2 0.352 -0.327  0.00604 0.352 0.352
    #>  3 1              3 0.270 -0.292  0.0134  0.270 0.270
    #>  4 1              4 0.521 -0.257  0.0274  0.521 0.521
    #>  5 1              5 0.907 -0.222  0.0524  0.907 0.907
    #>  6 1              6 0.297 -0.188  0.0931  0.297 0.297
    #>  7 1              7 0.974 -0.153  0.154   0.974 0.974
    #>  8 1              8 0.145 -0.118  0.239   0.145 0.145
    #>  9 1              9 0.897 -0.0836 0.345   0.897 0.897
    #> 10 1             10 0.874 -0.0489 0.467   0.874 0.874
    #> # ℹ 40 more rows