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This function will generate n random points from a lognormal distribution with a user provided, .meanlog, .sdlog, and number of random simulations to be produced. The function returns a tibble with the simulation number column the x column which corresponds 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_lognormal(
  .n = 50,
  .meanlog = 0,
  .sdlog = 1,
  .num_sims = 1,
  .return_tibble = TRUE
)

Arguments

.n

The number of randomly generated points you want.

.meanlog

Mean of the distribution on the log scale with default 0

.sdlog

Standard deviation of the distribution on the log scale with default 1

.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::rlnorm(), and its underlying p, d, and q functions. For more information please see stats::rlnorm()

Author

Steven P. Sanderson II, MPH

Examples

tidy_lognormal()
#> # A tibble: 50 × 7
#>    sim_number     x     y      dx       dy     p     q
#>    <fct>      <int> <dbl>   <dbl>    <dbl> <dbl> <dbl>
#>  1 1              1 4.14  -1.74   0.000468 0.922 4.14 
#>  2 1              2 0.387 -1.54   0.00141  0.171 0.387
#>  3 1              3 3.86  -1.33   0.00379  0.912 3.86 
#>  4 1              4 3.34  -1.13   0.00917  0.886 3.34 
#>  5 1              5 3.15  -0.924  0.0200   0.875 3.15 
#>  6 1              6 2.10  -0.719  0.0393   0.771 2.10 
#>  7 1              7 0.964 -0.515  0.0698   0.485 0.964
#>  8 1              8 1.86  -0.310  0.113    0.732 1.86 
#>  9 1              9 0.677 -0.105  0.166    0.348 0.677
#> 10 1             10 0.734  0.0995 0.223    0.379 0.734
#> # ℹ 40 more rows