
Tidy Randomly Generated Bernoulli Distribution Tibble
Source:R/random-tidy-bernoulli.R
tidy_bernoulli.Rd
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 ofn
for the current simulation.y
The randomly generated data point.dx
Thex
value from thestats::density()
function.dy
They
value from thestats::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.
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.
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.
See also
https://en.wikipedia.org/wiki/Bernoulli_distribution
Other Discrete Distribution:
tidy_binomial()
,
tidy_hypergeometric()
,
tidy_negative_binomial()
,
tidy_poisson()
,
tidy_zero_truncated_binomial()
,
tidy_zero_truncated_negative_binomial()
,
tidy_zero_truncated_poisson()
Other Bernoulli:
util_bernoulli_param_estimate()
,
util_bernoulli_stats_tbl()
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