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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  0.706 -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