
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 0.730 -0.468 0.00216 0.534 0.730
#> 2 1 2 1.01 -0.351 0.0111 0.670 1.01
#> 3 1 3 0.689 -0.234 0.0426 0.509 0.689
#> 4 1 4 0.717 -0.117 0.125 0.526 0.717
#> 5 1 5 0.238 -0.000464 0.282 0.0999 0.238
#> 6 1 6 0.940 0.117 0.502 0.643 0.940
#> 7 1 7 1.52 0.233 0.722 0.814 1.52
#> 8 1 8 0.420 0.350 0.870 0.290 0.420
#> 9 1 9 0.611 0.467 0.914 0.455 0.611
#> 10 1 10 1.29 0.584 0.861 0.764 1.29
#> # ℹ 40 more rows