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.
Usage
tidy_beta(
.n = 50,
.shape1 = 1,
.shape2 = 1,
.ncp = 0,
.num_sims = 1,
.return_tibble = TRUE
)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.
- .return_tibble
A logical value indicating whether to return the result as a tibble. Default is TRUE.
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_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
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 0.359 -0.353 0.00273 0.359 0.359
#> 2 1 2 0.988 -0.318 0.00644 0.988 0.988
#> 3 1 3 0.295 -0.283 0.0141 0.295 0.295
#> 4 1 4 0.724 -0.248 0.0284 0.724 0.724
#> 5 1 5 0.729 -0.213 0.0532 0.729 0.729
#> 6 1 6 0.301 -0.178 0.0925 0.301 0.301
#> 7 1 7 0.423 -0.143 0.149 0.423 0.423
#> 8 1 8 1.00 -0.107 0.224 1.00 1.00
#> 9 1 9 0.816 -0.0724 0.315 0.816 0.816
#> 10 1 10 0.784 -0.0373 0.416 0.784 0.784
#> # ℹ 40 more rows
