This function will generate n
random points from a weibull
distribution with a user provided, .shape
, .scale
, 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.
- .shape
Shape parameter defaults to 0.
- .scale
Scale parameter defaults to 1.
- .num_sims
The number of randomly generated simulations you want.
Details
This function uses the underlying stats::rweibull()
, and its underlying
p
, d
, and q
functions. For more information please see stats::rweibull()
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_lognormal()
,
tidy_normal()
,
tidy_paralogistic()
,
tidy_pareto1()
,
tidy_pareto()
,
tidy_t()
,
tidy_uniform()
,
tidy_zero_truncated_geometric()
Other Weibull:
tidy_inverse_weibull()
,
util_weibull_param_estimate()
,
util_weibull_stats_tbl()
Examples
tidy_weibull()
#> # A tibble: 50 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 1.63 -1.03 0.00253 0.805 1.63
#> 2 1 2 0.113 -0.890 0.00844 0.107 0.113
#> 3 1 3 0.519 -0.748 0.0242 0.405 0.519
#> 4 1 4 0.321 -0.607 0.0596 0.275 0.321
#> 5 1 5 4.86 -0.465 0.126 0.992 4.86
#> 6 1 6 0.190 -0.323 0.229 0.173 0.190
#> 7 1 7 0.136 -0.181 0.361 0.127 0.136
#> 8 1 8 0.317 -0.0397 0.493 0.271 0.317
#> 9 1 9 1.22 0.102 0.588 0.705 1.22
#> 10 1 10 2.14 0.244 0.618 0.883 2.14
#> # ℹ 40 more rows