
Tidy Randomly Generated Lognormal Distribution Tibble
Source:R/random-tidy-lognormal.R
tidy_lognormal.RdThis function will generate n random points from a lognormal
distribution with a user provided, .meanlog, .sdlog, 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.
- .meanlog
Mean of the distribution on the log scale with default 0
- .sdlog
Standard deviation of the distribution on the log scale with default 1
- .num_sims
The number of randomly generated simulations you want.
Details
This function uses the underlying stats::rlnorm(), and its underlying
p, d, and q functions. For more information please see stats::rlnorm()
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_normal(),
tidy_paralogistic(),
tidy_pareto1(),
tidy_pareto(),
tidy_t(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Lognormal:
util_lognormal_param_estimate(),
util_lognormal_stats_tbl()
Examples
tidy_lognormal()
#> # A tibble: 50 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 0.450 -1.21 0.00168 0.212 0.450
#> 2 1 2 0.414 -0.938 0.00936 0.189 0.414
#> 3 1 3 3.76 -0.668 0.0380 0.907 3.76
#> 4 1 4 0.749 -0.398 0.113 0.386 0.749
#> 5 1 5 1.27 -0.128 0.246 0.594 1.27
#> 6 1 6 0.196 0.142 0.398 0.0517 0.196
#> 7 1 7 0.895 0.412 0.489 0.456 0.895
#> 8 1 8 1.15 0.681 0.469 0.556 1.15
#> 9 1 9 0.977 0.951 0.373 0.491 0.977
#> 10 1 10 0.569 1.22 0.276 0.286 0.569
#> # ℹ 40 more rows