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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)

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()

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 -0.456 -5.87 0.000155 0.388  -0.456
#>  2 1              2  0.794 -5.60 0.000457 0.689   0.794
#>  3 1              3 -3.35  -5.34 0.00120  0.0339 -3.35 
#>  4 1              4 -0.298 -5.08 0.00278  0.426  -0.298
#>  5 1              5  0.106 -4.82 0.00575  0.527   0.106
#>  6 1              6  1.44  -4.55 0.0107   0.808   1.44 
#>  7 1              7 -0.103 -4.29 0.0179   0.474  -0.103
#>  8 1              8 -1.60  -4.03 0.0272   0.167  -1.60 
#>  9 1              9 -0.557 -3.77 0.0380   0.364  -0.557
#> 10 1             10  2.49  -3.51 0.0495   0.924   2.49 
#> # ℹ 40 more rows