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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, .return_tibble = TRUE)

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.

.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 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 -0.405  0.0292   0.9     0
#>  2 1              2     0 -0.368  0.0637   0.9     0
#>  3 1              3     0 -0.331  0.129    0.9     0
#>  4 1              4     1 -0.294  0.243    1       1
#>  5 1              5     0 -0.258  0.424    0.9     0
#>  6 1              6     0 -0.221  0.688    0.9     0
#>  7 1              7     0 -0.184  1.03     0.9     0
#>  8 1              8     1 -0.147  1.44     1       1
#>  9 1              9     0 -0.110  1.87     0.9     0
#> 10 1             10     0 -0.0727 2.25     0.9     0
#> # ℹ 40 more rows