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Tidy Randomly Generated Poisson Distribution Tibble

Source: R/random-tidy-poisson.R
tidy_poisson.Rd

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()

See also

https://r-coder.com/poisson-distribution-r/

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

Other Poisson: tidy_zero_truncated_poisson(), util_poisson_param_estimate(), util_poisson_stats_tbl(), util_zero_truncated_poisson_param_estimate(), util_zero_truncated_poisson_stats_tbl()

Other Discrete Distribution: tidy_bernoulli(), tidy_binomial(), tidy_geometric(), tidy_hypergeometric(), tidy_negative_binomial(), tidy_zero_truncated_binomial(), tidy_zero_truncated_negative_binomial(), tidy_zero_truncated_poisson()

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 -0.691  0.00503 0.736     1
#>  2 1              2     2 -0.561  0.0234  0.920     2
#>  3 1              3     0 -0.431  0.0787  0.368     0
#>  4 1              4     0 -0.300  0.193   0.368     0
#>  5 1              5     1 -0.170  0.343   0.736     1
#>  6 1              6     1 -0.0398 0.443   0.736     1
#>  7 1              7     4  0.0904 0.417   0.996     4
#>  8 1              8     2  0.221  0.287   0.920     2
#>  9 1              9     2  0.351  0.158   0.920     2
#> 10 1             10     1  0.481  0.120   0.736     1
#> # ℹ 40 more rows