
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 1.06 -1.58 0.000877 0.522 1.06
#> 2 1 2 3.08 -1.23 0.00545 0.870 3.08
#> 3 1 3 1.50 -0.884 0.0241 0.658 1.50
#> 4 1 4 0.722 -0.537 0.0767 0.373 0.722
#> 5 1 5 0.835 -0.190 0.177 0.429 0.835
#> 6 1 6 0.557 0.158 0.302 0.279 0.557
#> 7 1 7 0.176 0.505 0.386 0.0412 0.176
#> 8 1 8 1.11 0.852 0.385 0.542 1.11
#> 9 1 9 3.27 1.20 0.312 0.882 3.27
#> 10 1 10 0.267 1.55 0.221 0.0935 0.267
#> # ℹ 40 more rows