Tidy Randomly Generated Beta Distribution Tibble — tidy_beta • TidyDensity
Description: This function will generate n random points from a beta distribution with a user provided, .shape1, .shape2, .ncp or non-centrality parameter, 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 Beta Distribution Tibble

Source: `R/random-tidy-beta.R`
`tidy_beta.Rd`

This function will generate `n` random points from a beta distribution with a user provided, `.shape1`, `.shape2`, `.ncp` or `non-centrality parameter`, 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_beta(.n = 50, .shape1 = 1, .shape2 = 1, .ncp = 0, .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.

.ncp

  The `non-centrality parameter` of the Beta distribution.

.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

Other Continuous Distribution: `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_pareto()`, `tidy_t()`, `tidy_uniform()`, `tidy_weibull()`, `tidy_zero_truncated_geometric()`

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

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_beta()
    #> # A tibble: 50 × 7
    #>    sim_number     x      y       dx      dy      p      q
    #>    <fct>      <int>  <dbl>    <dbl>   <dbl>  <dbl>  <dbl>
    #>  1 1              1 0.693  -0.297   0.00125 0.693  0.693
    #>  2 1              2 0.0303 -0.264   0.00310 0.0303 0.0303
    #>  3 1              3 0.250  -0.232   0.00714 0.250  0.250
    #>  4 1              4 0.688  -0.199   0.0152  0.688  0.688
    #>  5 1              5 0.588  -0.166   0.0298  0.588  0.588
    #>  6 1              6 0.283  -0.134   0.0544  0.283  0.283
    #>  7 1              7 0.525  -0.101   0.0922  0.525  0.525
    #>  8 1              8 0.102  -0.0685  0.146   0.102  0.102
    #>  9 1              9 0.231  -0.0359  0.215   0.231  0.231
    #> 10 1             10 0.622  -0.00323 0.299   0.622  0.622
    #> # ℹ 40 more rows