
Tidy Randomly Generated Generalized Beta Distribution Tibble
Source:R/random-tidy-general-beta.R
tidy_generalized_beta.Rd
This 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_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.
Usage
tidy_generalized_beta(
.n = 50,
.shape1 = 1,
.shape2 = 1,
.shape3 = 1,
.rate = 1,
.scale = 1/.rate,
.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.
- .shape3
A non-negative parameter of the Beta distribution.
- .rate
An alternative way to specify the
.scale
parameter.- .scale
Must be strictly positive.
- .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
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_pareto()
,
tidy_pareto1()
,
tidy_t()
,
tidy_triangular()
,
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 0.781 -0.387 0.00215 0.781 0.781
#> 2 1 2 0.713 -0.352 0.00501 0.713 0.713
#> 3 1 3 0.193 -0.316 0.0109 0.193 0.193
#> 4 1 4 0.0543 -0.280 0.0222 0.0543 0.0543
#> 5 1 5 0.485 -0.244 0.0420 0.485 0.485
#> 6 1 6 0.0240 -0.208 0.0743 0.0240 0.0240
#> 7 1 7 0.204 -0.172 0.123 0.204 0.204
#> 8 1 8 0.870 -0.136 0.191 0.870 0.870
#> 9 1 9 0.463 -0.100 0.278 0.463 0.463
#> 10 1 10 0.770 -0.0646 0.380 0.770 0.770
#> # ℹ 40 more rows