Tidy Randomly Generated Gamma Distribution Tibble — tidy_gamma • TidyDensity
Description: This function will generate n random points from a gamma 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 Gamma Distribution Tibble

Source: `R/random-tidy-gamma.R`
`tidy_gamma.Rd`

This function will generate `n` random points from a gamma 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_gamma(.n = 50, .shape = 1, .scale = 0.3, .num_sims = 1)

## Arguments

.n

  The number of randomly generated points you want.

.shape

  This is strictly 0 to infinity.

.scale

  The standard deviation of the randomly generated data. This is strictly from 0 to infinity.

.num_sims

  The number of randomly generated simulations you want.

## Value

A tibble of randomly generated data.

## Details

This function uses the underlying `stats::rgamma()`, and its underlying `p`, `d`, and `q` functions. For more information please see `stats::rgamma()`

## See also

https://www.statology.org/fit-gamma-distribution-to-dataset-in-r/

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

Other Continuous Distribution: `tidy_beta()`, `tidy_burr()`, `tidy_cauchy()`, `tidy_chisquare()`, `tidy_exponential()`, `tidy_f()`, `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 Gamma: `tidy_inverse_gamma()`, `util_gamma_param_estimate()`, `util_gamma_stats_tbl()`

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_gamma()
    #> # A tibble: 50 × 7
    #>    sim_number     x       y      dx      dy      p       q
    #>    <fct>      <int>   <dbl>   <dbl>   <dbl>  <dbl>   <dbl>
    #>  1 1              1 0.139   -0.228  0.00401 0.370  0.139
    #>  2 1              2 0.0314  -0.201  0.0114  0.0993 0.0314
    #>  3 1              3 0.623   -0.175  0.0289  0.875  0.623
    #>  4 1              4 0.225   -0.148  0.0657  0.528  0.225
    #>  5 1              5 0.370   -0.121  0.135   0.708  0.370
    #>  6 1              6 0.114   -0.0945 0.250   0.315  0.114
    #>  7 1              7 0.157   -0.0679 0.421   0.407  0.157
    #>  8 1              8 0.00425 -0.0412 0.649   0.0141 0.00425
    #>  9 1              9 0.0915  -0.0145 0.924   0.263  0.0915
    #> 10 1             10 0.134    0.0122 1.22    0.360  0.134
    #> # ℹ 40 more rows