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_geometric(), 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 -2.51   -5.46 0.000171 0.0750 -2.51  
#>  2 1              2 -0.577  -5.21 0.000627 0.360  -0.577 
#>  3 1              3 -0.454  -4.96 0.00184  0.388  -0.454 
#>  4 1              4 -1.12   -4.71 0.00434  0.247  -1.12  
#>  5 1              5  0.733  -4.47 0.00821  0.675   0.733 
#>  6 1              6  0.391  -4.22 0.0125   0.597   0.391 
#>  7 1              7  0.0979 -3.97 0.0156   0.524   0.0979
#>  8 1              8  1.99   -3.72 0.0168   0.880   1.99  
#>  9 1              9 -1.68   -3.47 0.0180   0.157  -1.68  
#> 10 1             10 -0.670  -3.22 0.0234   0.338  -0.670 
#> # ℹ 40 more rows