This function will generate n
random points from a
pareto 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
Must be positive.
- .scale
Must be positive.
- .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 actuar::rpareto()
, and its underlying
p
, d
, and q
functions. For more information please see actuar::rpareto()
See also
https://openacttexts.github.io/Loss-Data-Analytics/ChapSummaryDistributions.html
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_t()
,
tidy_triangular()
,
tidy_uniform()
,
tidy_weibull()
,
tidy_zero_truncated_geometric()
Other Pareto:
tidy_generalized_pareto()
,
tidy_inverse_pareto()
,
tidy_pareto1()
,
util_pareto1_aic()
,
util_pareto1_param_estimate()
,
util_pareto1_stats_tbl()
,
util_pareto_param_estimate()
,
util_pareto_stats_tbl()
Examples
tidy_pareto()
#> # A tibble: 50 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 0.00692 -0.0131 0.139 0.488 0.00692
#> 2 1 2 0.00881 -0.0115 0.403 0.570 0.00881
#> 3 1 3 0.00137 -0.00998 1.04 0.127 0.00137
#> 4 1 4 0.00659 -0.00843 2.40 0.472 0.00659
#> 5 1 5 0.0151 -0.00687 4.95 0.755 0.0151
#> 6 1 6 0.000987 -0.00532 9.18 0.0935 0.000987
#> 7 1 7 0.00428 -0.00376 15.3 0.343 0.00428
#> 8 1 8 0.0203 -0.00221 23.0 0.843 0.0203
#> 9 1 9 0.000921 -0.000652 31.4 0.0876 0.000921
#> 10 1 10 0.00140 0.000903 39.2 0.130 0.00140
#> # ℹ 40 more rows