
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 0.340 -0.300 0.00273 0.208 0.340
#> 2 1 2 0.577 -0.187 0.0220 0.429 0.577
#> 3 1 3 0.165 -0.0734 0.109 0.0352 0.165
#> 4 1 4 0.961 0.0399 0.345 0.652 0.961
#> 5 1 5 0.614 0.153 0.716 0.457 0.614
#> 6 1 6 0.635 0.266 1.04 0.472 0.635
#> 7 1 7 0.560 0.380 1.15 0.416 0.560
#> 8 1 8 1.09 0.493 1.09 0.701 1.09
#> 9 1 9 4.17 0.606 0.924 0.982 4.17
#> 10 1 10 4.79 0.720 0.724 0.988 4.79
#> # ℹ 40 more rows