
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 1.32 -0.932 0.00180 0.732 1.32
#> 2 1 2 0.935 -0.800 0.00630 0.608 0.935
#> 3 1 3 0.00817 -0.669 0.0186 0.00813 0.00817
#> 4 1 4 1.03 -0.537 0.0468 0.642 1.03
#> 5 1 5 0.00604 -0.406 0.100 0.00602 0.00604
#> 6 1 6 1.56 -0.275 0.185 0.791 1.56
#> 7 1 7 0.969 -0.143 0.294 0.621 0.969
#> 8 1 8 1.09 -0.0116 0.410 0.663 1.09
#> 9 1 9 0.256 0.120 0.507 0.226 0.256
#> 10 1 10 2.35 0.251 0.569 0.905 2.35
#> # ℹ 40 more rows