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This function will generate n random points from a Poisson distribution with a user provided, .lambda, 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_poisson(.n = 50, .lambda = 1, .num_sims = 1, .return_tibble = TRUE)

Arguments

.n

The number of randomly generated points you want.

.lambda

A vector of non-negative means.

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

Author

Steven P. Sanderson II, MPH

Examples

tidy_poisson()
#> # A tibble: 50 × 7
#>    sim_number     x     y     dx      dy     p     q
#>    <fct>      <int> <int>  <dbl>   <dbl> <dbl> <dbl>
#>  1 1              1     1 -1.13  0.00453 0.736     1
#>  2 1              2     0 -1.02  0.0102  0.368     0
#>  3 1              3     0 -0.913 0.0213  0.368     0
#>  4 1              4     0 -0.805 0.0408  0.368     0
#>  5 1              5     1 -0.698 0.0721  0.736     1
#>  6 1              6     1 -0.591 0.118   0.736     1
#>  7 1              7     1 -0.484 0.177   0.736     1
#>  8 1              8     1 -0.376 0.245   0.736     1
#>  9 1              9     1 -0.269 0.314   0.736     1
#> 10 1             10     2 -0.162 0.371   0.920     2
#> # ℹ 40 more rows