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Generate Tidy Data from Triangular Distribution

Source: R/random-tidy-triangular.R
tidy_triangular.Rd

This 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.

Value

A tibble of randomly generated data.

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_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()

Author

Steven P. Sanderson II, MPH

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 0.654 -0.270    0.00145 0.760  0.654
#>  2 1              2 0.168 -0.240    0.00376 0.0567 0.168
#>  3 1              3 0.164 -0.210    0.00880 0.0540 0.164
#>  4 1              4 0.310 -0.180    0.0187  0.192  0.310
#>  5 1              5 0.653 -0.150    0.0361  0.759  0.653
#>  6 1              6 0.529 -0.119    0.0639  0.557  0.529
#>  7 1              7 0.127 -0.0894   0.104   0.0325 0.127
#>  8 1              8 0.584 -0.0593   0.157   0.654  0.584
#>  9 1              9 0.496 -0.0293   0.224   0.493  0.496
#> 10 1             10 0.344  0.000812 0.302   0.237  0.344
#> # ℹ 40 more rows