Tidy Randomly Generated Logistic Distribution Tibble — tidy_logistic • TidyDensity
Description: 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.

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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)

## 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.

## 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_pareto1()`, `tidy_pareto()`, `tidy_t()`, `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  0.706 -8.73 0.000129 0.669  0.706
    #>  2 1              2  3.08  -8.45 0.000406 0.956  3.08
    #>  3 1              3  0.231 -8.16 0.00107  0.558  0.231
    #>  4 1              4 -0.280 -7.88 0.00242  0.431 -0.280
    #>  5 1              5 -0.987 -7.60 0.00465  0.271 -0.987
    #>  6 1              6  0.731 -7.31 0.00765  0.675  0.731
    #>  7 1              7  0.148 -7.03 0.0109   0.537  0.148
    #>  8 1              8 -1.10  -6.75 0.0137   0.250 -1.10
    #>  9 1              9  1.35  -6.46 0.0155   0.794  1.35
    #> 10 1             10 -0.170 -6.18 0.0163   0.458 -0.170
    #> # ℹ 40 more rows