This function will generate n random points from a beta
distribution with a user provided, .shape1, .shape2, .ncp or non-centrality parameter,
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_numberThe current simulation number.xThe current value ofnfor the current simulation.yThe randomly generated data point.dxThexvalue from thestats::density()function.dyTheyvalue from thestats::density()function.pThe values from the resulting p_ function of the distribution family.qThe values from the resulting q_ function of the distribution family.
Arguments
- .n
The number of randomly generated points you want.
- .shape1
A non-negative parameter of the Beta distribution.
- .shape2
A non-negative parameter of the Beta distribution.
- .ncp
The
non-centrality parameterof the Beta distribution.- .num_sims
The number of randomly generated simulations you want.
Details
This function uses the underlying stats::rbeta(), and its underlying
p, d, and q functions. For more information please see stats::rbeta()
See also
https://statisticsglobe.com/beta-distribution-in-r-dbeta-pbeta-qbeta-rbeta
https://en.wikipedia.org/wiki/Beta_distribution
Other Continuous Distribution:
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
tidy_generalized_beta(),
tidy_generalized_pareto(),
tidy_geometric(),
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto1(),
tidy_pareto(),
tidy_t(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Beta:
tidy_generalized_beta(),
util_beta_param_estimate(),
util_beta_stats_tbl()
Examples
tidy_beta()
#> # A tibble: 50 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 0.170 -0.307 0.00275 0.170 0.170
#> 2 1 2 0.486 -0.274 0.00702 0.486 0.486
#> 3 1 3 0.267 -0.242 0.0163 0.267 0.267
#> 4 1 4 0.465 -0.209 0.0345 0.465 0.465
#> 5 1 5 0.629 -0.177 0.0665 0.629 0.629
#> 6 1 6 0.171 -0.144 0.117 0.171 0.171
#> 7 1 7 0.617 -0.112 0.190 0.617 0.617
#> 8 1 8 0.00861 -0.0793 0.282 0.00861 0.00861
#> 9 1 9 0.0729 -0.0468 0.387 0.0729 0.0729
#> 10 1 10 0.715 -0.0143 0.493 0.715 0.715
#> # ℹ 40 more rows
