
Tidy Randomly Generated Exponential Distribution Tibble
Source:R/random-tidy-exponential.R
tidy_exponential.Rd
This function will generate n
random points from a exponential
distribution with a user provided, .rate
, 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.
- .rate
A vector of rates
- .num_sims
The number of randomly generated simulations you want.
Details
This function uses the underlying stats::rexp()
, and its underlying
p
, d
, and q
functions. For more information please see stats::rexp()
See also
https://www.itl.nist.gov/div898/handbook/eda/section3/eda3667.htm
Other Continuous Distribution:
tidy_beta()
,
tidy_burr()
,
tidy_cauchy()
,
tidy_chisquare()
,
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_lognormal()
,
tidy_normal()
,
tidy_paralogistic()
,
tidy_pareto1()
,
tidy_pareto()
,
tidy_t()
,
tidy_uniform()
,
tidy_weibull()
,
tidy_zero_truncated_geometric()
Other Exponential:
tidy_inverse_exponential()
,
util_exponential_param_estimate()
,
util_exponential_stats_tbl()
Examples
tidy_exponential()
#> # A tibble: 50 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 1.05 -0.930 0.000941 0.651 1.05
#> 2 1 2 0.483 -0.775 0.00412 0.383 0.483
#> 3 1 3 2.42 -0.621 0.0146 0.911 2.42
#> 4 1 4 1.08 -0.466 0.0423 0.659 1.08
#> 5 1 5 1.71 -0.312 0.101 0.819 1.71
#> 6 1 6 0.348 -0.157 0.200 0.294 0.348
#> 7 1 7 0.421 -0.00282 0.333 0.343 0.421
#> 8 1 8 1.17 0.152 0.470 0.688 1.17
#> 9 1 9 0.821 0.306 0.568 0.560 0.821
#> 10 1 10 0.0645 0.461 0.596 0.0625 0.0645
#> # ℹ 40 more rows