
Tidy Randomly Generated Inverse Gaussian Distribution Tibble
Source:R/random-tidy-normal-inverse.R
tidy_inverse_normal.RdThis function will generate n random points from an Inverse Gaussian
distribution with a user provided, .mean, .shape, .dispersionThe function
returns a tibble with the simulation number column the x column which corresponds
to the n randomly generated points.
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.
- .mean
Must be strictly positive.
- .shape
Must be strictly positive.
- .dispersion
An alternative way to specify the
.shape.- .num_sims
The number of randomly generated simulations you want.
Details
This function uses the underlying actuar::rinvgauss(). For
more information please see rinvgauss()
See also
Other Continuous Distribution:
tidy_beta(),
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_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 Gaussian:
tidy_normal(),
util_normal_param_estimate(),
util_normal_stats_tbl()
Other Inverse Distribution:
tidy_inverse_burr(),
tidy_inverse_exponential(),
tidy_inverse_gamma(),
tidy_inverse_pareto(),
tidy_inverse_weibull()
Examples
tidy_inverse_normal()
#> # A tibble: 50 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 0.296 -1.03 0.00135 0.161 0.296
#> 2 1 2 0.286 -0.882 0.00426 0.150 0.286
#> 3 1 3 0.336 -0.739 0.0118 0.204 0.336
#> 4 1 4 0.797 -0.595 0.0290 0.573 0.797
#> 5 1 5 1.01 -0.451 0.0623 0.671 1.01
#> 6 1 6 2.09 -0.307 0.118 0.895 2.09
#> 7 1 7 0.362 -0.164 0.199 0.231 0.362
#> 8 1 8 1.15 -0.0200 0.298 0.722 1.15
#> 9 1 9 0.130 0.124 0.399 0.0145 0.130
#> 10 1 10 0.244 0.267 0.482 0.106 0.244
#> # ℹ 40 more rows