
Tidy Randomly Generated Logistic Distribution Tibble
Source:R/random-tidy-logistic.R
tidy_logistic.Rd
This 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_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.
- .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 -0.300 -5.83 0.000150 0.425 -0.300
#> 2 1 2 2.34 -5.59 0.000436 0.912 2.34
#> 3 1 3 0.455 -5.35 0.00112 0.612 0.455
#> 4 1 4 -2.21 -5.12 0.00255 0.0987 -2.21
#> 5 1 5 1.21 -4.88 0.00515 0.771 1.21
#> 6 1 6 0.135 -4.64 0.00933 0.534 0.135
#> 7 1 7 3.53 -4.40 0.0152 0.971 3.53
#> 8 1 8 -1.56 -4.16 0.0225 0.173 -1.56
#> 9 1 9 1.94 -3.92 0.0306 0.875 1.94
#> 10 1 10 -3.32 -3.68 0.0386 0.0349 -3.32
#> # ℹ 40 more rows