
Tidy Randomly Generated Logistic Distribution Tibble
Source:R/random-tidy-logistic.R
tidy_logistic.RdThis function will generate n random points from a logistic
distribution with a user provided, .location, .scale, and number of
random simulations to be produced. The function returns a tibble with the
simulation number column the x column which corresonds 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.
- .location
The location parameter
- .scale
The scale parameter
- .num_sims
The number of randomly generated simulations you want.
- .return_tibble
A logical value indicating whether to return the result as a tibble. Default is TRUE.
Details
This function uses the underlying stats::rlogis(), and its underlying
p, d, and q functions. For more information please see stats::rlogis()
See also
https://en.wikipedia.org/wiki/Logistic_distribution
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_lognormal(),
tidy_normal(),
tidy_paralogistic(),
tidy_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_triangular(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Logistic:
tidy_paralogistic(),
util_logistic_param_estimate(),
util_logistic_stats_tbl()
Examples
tidy_logistic()
#> # A tibble: 50 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 -2.51 -5.46 0.000171 0.0750 -2.51
#> 2 1 2 -0.577 -5.21 0.000627 0.360 -0.577
#> 3 1 3 -0.454 -4.96 0.00184 0.388 -0.454
#> 4 1 4 -1.12 -4.71 0.00434 0.247 -1.12
#> 5 1 5 0.733 -4.47 0.00821 0.675 0.733
#> 6 1 6 0.391 -4.22 0.0125 0.597 0.391
#> 7 1 7 0.0979 -3.97 0.0156 0.524 0.0979
#> 8 1 8 1.99 -3.72 0.0168 0.880 1.99
#> 9 1 9 -1.68 -3.47 0.0180 0.157 -1.68
#> 10 1 10 -0.670 -3.22 0.0234 0.338 -0.670
#> # ℹ 40 more rows