This function will generate n
random points from a Burr
distribution with a user provided, .shape1
, .shape2
, .scale
, .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.
- .shape1
Must be strictly positive.
- .shape2
Must be strictly positive.
- .rate
An alternative way to specify the
.scale
.- .scale
Must be strictly positive.
- .num_sims
The number of randomly generated simulations you want.
Details
This function uses the underlying actuar::rburr()
, and its underlying
p
, d
, and q
functions. For more information please see actuar::rburr()
See also
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
Other Continuous Distribution:
tidy_beta()
,
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_weibull()
,
tidy_zero_truncated_geometric()
Other Burr:
tidy_inverse_burr()
Examples
tidy_burr()
#> # A tibble: 50 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 0.00855 -1.65 0.000682 0.00848 0.00855
#> 2 1 2 1.51 -0.706 0.0424 0.601 1.51
#> 3 1 3 9.71 0.241 0.300 0.907 9.71
#> 4 1 4 31.2 1.19 0.355 0.969 31.2
#> 5 1 5 1.01 2.14 0.102 0.502 1.01
#> 6 1 6 1.22 3.08 0.0399 0.550 1.22
#> 7 1 7 6.17 4.03 0.0145 0.860 6.17
#> 8 1 8 0.129 4.98 0.0188 0.114 0.129
#> 9 1 9 0.641 5.93 0.0356 0.391 0.641
#> 10 1 10 1.45 6.87 0.0193 0.592 1.45
#> # … with 40 more rows