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This function will generate n random points from a Bernoulli distribution with a user provided, .prob, 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_bernoulli(.n = 50, .prob = 0.1, .num_sims = 1)

Arguments

.n

The number of randomly generated points you want.

.prob

The probability of success/failure.

.num_sims

The number of randomly generated simulations you want.

Value

A tibble of randomly generated data.

Details

This function uses the rbinom(), and its underlying p, d, and q functions. The Bernoulli distribution is a special case of the Binomial distribution with size = 1 hence this is why the binom functions are used and set to size = 1.

Author

Steven P. Sanderson II, MPH

Examples

tidy_bernoulli()
#> # A tibble: 50 × 7
#>    sim_number     x     y      dx     dy     p     q
#>    <fct>      <int> <int>   <dbl>  <dbl> <dbl> <dbl>
#>  1 1              1     0 -0.374  0.0324   0.9     0
#>  2 1              2     0 -0.338  0.0732   0.9     0
#>  3 1              3     0 -0.303  0.152    0.9     0
#>  4 1              4     0 -0.267  0.292    0.9     0
#>  5 1              5     1 -0.231  0.517    1       1
#>  6 1              6     0 -0.196  0.842    0.9     0
#>  7 1              7     0 -0.160  1.27     0.9     0
#>  8 1              8     1 -0.124  1.75     1       1
#>  9 1              9     0 -0.0887 2.24     0.9     0
#> 10 1             10     0 -0.0531 2.63     0.9     0
#> # ℹ 40 more rows