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_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 0.00866 -0.0112 0.157 0.564 0.00866
#> 2 1 2 0.0148 -0.00984 0.452 0.749 0.0148
#> 3 1 3 0.000112 -0.00851 1.16 0.0111 0.000112
#> 4 1 4 0.00653 -0.00718 2.64 0.469 0.00653
#> 5 1 5 0.00270 -0.00585 5.36 0.234 0.00270
#> 6 1 6 0.0158 -0.00453 9.74 0.769 0.0158
#> 7 1 7 0.00248 -0.00320 15.9 0.218 0.00248
#> 8 1 8 0.0112 -0.00187 23.4 0.654 0.0112
#> 9 1 9 0.0149 -0.000541 31.2 0.752 0.0149
#> 10 1 10 0.0183 0.000787 38.0 0.814 0.0183
#> # ℹ 40 more rows