Tidy Randomly Generated Logistic Distribution Tibble

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()

See also

https://en.wikipedia.org/wiki/Logistic_distribution

Other Continuous Distribution: tidy_beta(), tidy_burr(), tidy_cauchy(), tidy_chisquare(), tidy_exponential(), tidy_f(), tidy_gamma(), tidy_generalized_beta(), tidy_generalized_pareto(), tidy_inverse_burr(), tidy_inverse_exponential(), tidy_inverse_gamma(), tidy_inverse_normal(), tidy_inverse_pareto(), tidy_inverse_weibull(), tidy_lognormal(), tidy_normal(), tidy_paralogistic(), tidy_pareto(), tidy_pareto1(), tidy_t(), tidy_triangular(), tidy_uniform(), tidy_weibull(), tidy_zero_truncated_geometric()

Other Logistic: tidy_paralogistic(), util_logistic_param_estimate(), util_logistic_stats_tbl()

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 -0.864  -5.97 0.000143 0.296  -0.864 
#>  2 1              2 -1.57   -5.74 0.000418 0.173  -1.57  
#>  3 1              3  3.61   -5.50 0.00106  0.974   3.61  
#>  4 1              4  1.80   -5.26 0.00234  0.858   1.80  
#>  5 1              5  0.203  -5.02 0.00450  0.550   0.203 
#>  6 1              6  2.33   -4.79 0.00763  0.911   2.33  
#>  7 1              7 -2.99   -4.55 0.0116   0.0479 -2.99  
#>  8 1              8  2.40   -4.31 0.0160   0.917   2.40  
#>  9 1              9  0.0443 -4.07 0.0211   0.511   0.0443
#> 10 1             10  0.751  -3.83 0.0272   0.679   0.751 
#> # ℹ 40 more rows