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# Tidy Randomly Generated Logistic Distribution Tibble

Source: `R/random-tidy-logistic.R`
`tidy_logistic.Rd`

This function will generate `n` random points from a logistic distribution with a user provided, `.location`, `.scale`, and number of random simulations to be produced. The function returns a tibble with the simulation number column the x column which corresonds 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_logistic(
      .n = 50,
      .location = 0,
      .scale = 1,
      .num_sims = 1,
      .return_tibble = TRUE
    )

## Arguments

.n

  The number of randomly generated points you want.

.location

  The location parameter

.scale

  The scale parameter

.num_sims

  The number of randomly generated simulations you want.

.return_tibble

  A logical value indicating whether to return the result as a tibble. Default is TRUE.

## Value

A tibble of randomly generated data.

## Details

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

## See also

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

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_lognormal()`, `tidy_normal()`, `tidy_paralogistic()`, `tidy_pareto()`, `tidy_pareto1()`, `tidy_t()`, `tidy_triangular()`, `tidy_uniform()`, `tidy_weibull()`, `tidy_zero_truncated_geometric()`

Other Logistic: `tidy_paralogistic()`, `util_logistic_param_estimate()`, `util_logistic_stats_tbl()`

## Author

Steven P. Sanderson II, MPH

## Examples

    tidy_logistic()
    #> # A tibble: 50 × 7
    #>    sim_number     x      y    dx       dy      p      q
    #>    <fct>      <int>  <dbl> <dbl>    <dbl>  <dbl>  <dbl>
    #>  1 1              1 -0.300 -5.83 0.000150 0.425  -0.300
    #>  2 1              2  2.34  -5.59 0.000436 0.912   2.34
    #>  3 1              3  0.455 -5.35 0.00112  0.612   0.455
    #>  4 1              4 -2.21  -5.12 0.00255  0.0987 -2.21
    #>  5 1              5  1.21  -4.88 0.00515  0.771   1.21
    #>  6 1              6  0.135 -4.64 0.00933  0.534   0.135
    #>  7 1              7  3.53  -4.40 0.0152   0.971   3.53
    #>  8 1              8 -1.56  -4.16 0.0225   0.173  -1.56
    #>  9 1              9  1.94  -3.92 0.0306   0.875   1.94
    #> 10 1             10 -3.32  -3.68 0.0386   0.0349 -3.32
    #> # ℹ 40 more rows