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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 -1.61  -6.10 0.000137 0.166  -1.61 
#>  2 1              2 -0.776 -5.88 0.000356 0.315  -0.776
#>  3 1              3 -1.09  -5.66 0.000826 0.251  -1.09 
#>  4 1              4 -1.98  -5.44 0.00171  0.121  -1.98 
#>  5 1              5 -0.198 -5.21 0.00317  0.451  -0.198
#>  6 1              6 -0.823 -4.99 0.00528  0.305  -0.823
#>  7 1              7 -0.299 -4.77 0.00790  0.426  -0.299
#>  8 1              8 -4.14  -4.55 0.0107   0.0157 -4.14 
#>  9 1              9 -0.848 -4.33 0.0134   0.300  -0.848
#> 10 1             10 -1.35  -4.11 0.0158   0.206  -1.35 
#> # ℹ 40 more rows