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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 -0.499 0.0215   0.9     0
#>  2 1              2     0 -0.458 0.0436   0.9     0
#>  3 1              3     0 -0.417 0.0830   0.9     0
#>  4 1              4     0 -0.377 0.149    0.9     0
#>  5 1              5     0 -0.336 0.251    0.9     0
#>  6 1              6     0 -0.295 0.399    0.9     0
#>  7 1              7     0 -0.254 0.598    0.9     0
#>  8 1              8     0 -0.214 0.843    0.9     0
#>  9 1              9     0 -0.173 1.12     0.9     0
#> 10 1             10     1 -0.132 1.40     1       1
#> # ℹ 40 more rows