
Tidy Randomly Generated Lognormal Distribution Tibble
Source:R/random-tidy-lognormal.R
tidy_lognormal.Rd
This 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_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.
- .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 0.210 -1.42 0.000994 0.0594 0.210
#> 2 1 2 1.48 -1.22 0.00319 0.652 1.48
#> 3 1 3 0.278 -1.03 0.00893 0.100 0.278
#> 4 1 4 2.13 -0.831 0.0218 0.775 2.13
#> 5 1 5 1.29 -0.635 0.0465 0.600 1.29
#> 6 1 6 2.08 -0.439 0.0872 0.768 2.08
#> 7 1 7 0.414 -0.243 0.144 0.189 0.414
#> 8 1 8 1.73 -0.0473 0.211 0.708 1.73
#> 9 1 9 1.43 0.149 0.275 0.641 1.43
#> 10 1 10 2.36 0.345 0.325 0.804 2.36
#> # ℹ 40 more rows