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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()

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