This function will generate n random points from a weibull
distribution with a user provided, .shape, .scale, 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_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.
- .shape
Shape parameter defaults to 0.
- .scale
Scale parameter defaults to 1.
- .num_sims
The number of randomly generated simulations you want.
Details
This function uses the underlying stats::rweibull(), and its underlying
p, d, and q functions. For more information please see stats::rweibull()
See also
https://www.itl.nist.gov/div898/handbook/eda/section3/eda3669.htm
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_normal(),
tidy_inverse_pareto(),
tidy_inverse_weibull(),
tidy_logistic(),
tidy_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto1(),
tidy_pareto(),
tidy_t(),
tidy_uniform(),
tidy_zero_truncated_geometric()
Other Weibull:
tidy_inverse_weibull(),
util_weibull_param_estimate(),
util_weibull_stats_tbl()
Examples
tidy_weibull()
#> # A tibble: 50 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 0.263 -0.936 0.00145 0.232 0.263
#> 2 1 2 0.503 -0.803 0.00514 0.395 0.503
#> 3 1 3 1.08 -0.670 0.0155 0.661 1.08
#> 4 1 4 0.414 -0.536 0.0398 0.339 0.414
#> 5 1 5 0.344 -0.403 0.0877 0.291 0.344
#> 6 1 6 0.0839 -0.270 0.166 0.0805 0.0839
#> 7 1 7 0.372 -0.137 0.275 0.311 0.372
#> 8 1 8 0.103 -0.00355 0.400 0.0980 0.103
#> 9 1 9 0.518 0.130 0.517 0.404 0.518
#> 10 1 10 0.0431 0.263 0.599 0.0422 0.0431
#> # ℹ 40 more rows
