Tidy Randomly Generated Inverse Gaussian Distribution Tibble
Source:R/random-tidy-normal-inverse.R
tidy_inverse_normal.Rd
This function will generate n
random points from an Inverse Gaussian
distribution with a user provided, .mean
, .shape
, .dispersion
The 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_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.
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 0.916 -0.541 0.00141 0.632 0.916
#> 2 1 2 1.03 -0.436 0.00557 0.681 1.03
#> 3 1 3 2.74 -0.331 0.0182 0.941 2.74
#> 4 1 4 1.94 -0.225 0.0497 0.878 1.94
#> 5 1 5 0.837 -0.120 0.113 0.594 0.837
#> 6 1 6 0.921 -0.0148 0.219 0.635 0.921
#> 7 1 7 0.569 0.0905 0.361 0.422 0.569
#> 8 1 8 0.456 0.196 0.516 0.325 0.456
#> 9 1 9 0.678 0.301 0.648 0.501 0.678
#> 10 1 10 1.83 0.406 0.731 0.865 1.83
#> # … with 40 more rows