
Tidy Randomly Generated Paralogistic Distribution Tibble
Source:R/random-tidy-paralogistic.R
tidy_paralogistic.Rd
This function will generate n
random points from a paralogistic
distribution with a user provided, .shape
, .rate
, .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 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::rparalogis()
, and its underlying
p
, d
, and q
functions. For more information please see actuar::rparalogis()
See also
https://en.wikipedia.org/wiki/Logistic_distribution
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_pareto1()
,
tidy_pareto()
,
tidy_t()
,
tidy_uniform()
,
tidy_weibull()
,
tidy_zero_truncated_geometric()
Other Logistic:
tidy_logistic()
,
util_logistic_param_estimate()
,
util_logistic_stats_tbl()
Examples
tidy_paralogistic()
#> # A tibble: 50 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 0.541 -3.36 0.000925 0.351 0.541
#> 2 1 2 6.19 -2.54 0.00696 0.861 6.19
#> 3 1 3 1.16 -1.71 0.0318 0.538 1.16
#> 4 1 4 0.565 -0.891 0.0882 0.361 0.565
#> 5 1 5 0.947 -0.0693 0.155 0.486 0.947
#> 6 1 6 0.0751 0.753 0.183 0.0699 0.0751
#> 7 1 7 1.20 1.57 0.162 0.545 1.20
#> 8 1 8 1.69 2.40 0.126 0.628 1.69
#> 9 1 9 4.92 3.22 0.0922 0.831 4.92
#> 10 1 10 3.65 4.04 0.0635 0.785 3.65
#> # ℹ 40 more rows