
Tidy Randomly Generated Generalized Beta Distribution Tibble
Source:R/random-tidy-general-beta.R
tidy_generalized_beta.RdThis function will generate n random points from a generalized beta
distribution with a user provided, .shape1, .shape2, .shape3, .rate, and/or
.sclae, 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_generalized_beta(
.n = 50,
.shape1 = 1,
.shape2 = 1,
.shape3 = 1,
.rate = 1,
.scale = 1/.rate,
.num_sims = 1
)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.
- .shape3
A non-negative parameter of the Beta distribution.
- .rate
An alternative way to specify the
.scaleparameter.- .scale
Must be strictly positive.
- .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
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Continuous Distribution:
tidy_beta(),
tidy_burr(),
tidy_cauchy(),
tidy_chisquare(),
tidy_exponential(),
tidy_f(),
tidy_gamma(),
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_beta(),
util_beta_param_estimate(),
util_beta_stats_tbl()
Examples
tidy_generalized_beta()
#> # A tibble: 50 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 0.758 -0.330 0.00205 0.758 0.758
#> 2 1 2 0.552 -0.296 0.00475 0.552 0.552
#> 3 1 3 0.710 -0.261 0.0102 0.710 0.710
#> 4 1 4 0.977 -0.226 0.0207 0.977 0.977
#> 5 1 5 0.652 -0.191 0.0390 0.652 0.652
#> 6 1 6 0.256 -0.157 0.0690 0.256 0.256
#> 7 1 7 0.435 -0.122 0.114 0.435 0.435
#> 8 1 8 0.970 -0.0873 0.177 0.970 0.970
#> 9 1 9 0.979 -0.0526 0.259 0.979 0.979
#> 10 1 10 0.883 -0.0179 0.355 0.883 0.883
#> # ℹ 40 more rows