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 0.581 -0.364 0.00213 0.581 0.581
#> 2 1 2 0.699 -0.328 0.00505 0.699 0.699
#> 3 1 3 0.862 -0.293 0.0111 0.862 0.862
#> 4 1 4 0.150 -0.258 0.0225 0.150 0.150
#> 5 1 5 0.228 -0.223 0.0427 0.228 0.228
#> 6 1 6 0.949 -0.188 0.0750 0.949 0.949
#> 7 1 7 0.990 -0.152 0.123 0.990 0.990
#> 8 1 8 0.356 -0.117 0.188 0.356 0.356
#> 9 1 9 0.636 -0.0820 0.269 0.636 0.636
#> 10 1 10 0.546 -0.0468 0.362 0.546 0.546
#> # … with 40 more rows