Generate Tidy Data from Triangular Distribution
Source:R/random-tidy-triangular.R
tidy_triangular.RdThis function generates tidy data from the triangular distribution.
Usage
tidy_triangular(
.n = 50,
.min = 0,
.max = 1,
.mode = 1/2,
.num_sims = 1,
.return_tibble = TRUE
)Arguments
- .n
The number of x values for each simulation.
- .min
The minimum value of the triangular distribution.
- .max
The maximum value of the triangular distribution.
- .mode
The mode (peak) value of the triangular distribution.
- .num_sims
The number of simulations to perform.
- .return_tibble
A logical value indicating whether to return the result as a tibble. Default is TRUE.
Details
The function takes parameters for the triangular distribution
(minimum, maximum, mode), the number of x values (n), the number of
simulations (num_sims), and an option to return the result as a tibble
(return_tibble). It performs various checks on the input parameters to ensure
validity. The result is a data frame or tibble with tidy data for
further analysis.
See also
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_pareto(),
tidy_pareto1(),
tidy_t(),
tidy_uniform(),
tidy_weibull(),
tidy_zero_truncated_geometric()
Other Triangular:
util_triangular_param_estimate(),
util_triangular_stats_tbl()
Examples
tidy_triangular(.return_tibble = TRUE)
#> # A tibble: 50 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 0.441 -0.155 0.00198 0.389 0.441
#> 2 1 2 0.332 -0.129 0.00505 0.221 0.332
#> 3 1 3 0.753 -0.103 0.0117 0.878 0.753
#> 4 1 4 0.808 -0.0770 0.0247 0.926 0.808
#> 5 1 5 0.780 -0.0509 0.0478 0.903 0.780
#> 6 1 6 0.0936 -0.0249 0.0845 0.0175 0.0936
#> 7 1 7 0.564 0.00118 0.137 0.620 0.564
#> 8 1 8 0.590 0.0272 0.207 0.664 0.590
#> 9 1 9 0.679 0.0533 0.292 0.794 0.679
#> 10 1 10 0.743 0.0793 0.388 0.868 0.743
#> # ℹ 40 more rows