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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 -0.548  -9.73 0.000110 0.366 -0.548 
#>  2 1              2  1.44   -9.38 0.000363 0.809  1.44  
#>  3 1              3  0.845  -9.02 0.000999 0.700  0.845 
#>  4 1              4  1.59   -8.67 0.00228  0.831  1.59  
#>  5 1              5  5.04   -8.32 0.00433  0.994  5.04  
#>  6 1              6 -1.93   -7.97 0.00683  0.126 -1.93  
#>  7 1              7 -0.476  -7.62 0.00896  0.383 -0.476 
#>  8 1              8 -0.0737 -7.27 0.00978  0.482 -0.0737
#>  9 1              9  1.22   -6.92 0.00887  0.772  1.22  
#> 10 1             10  1.42   -6.57 0.00673  0.806  1.42  
#> # … with 40 more rows