Tidy Randomly Generated Exponential Distribution Tibble

This function will generate n random points from a exponential distribution with a user provided, .rate, 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 of n for the current simulation.
y The randomly generated data point.
dx The x value from the stats::density() function.
dy The y value from the stats::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.

Usage

tidy_exponential(.n = 50, .rate = 1, .num_sims = 1, .return_tibble = TRUE)

Arguments

.n: The number of randomly generated points you want.
.rate: A vector of rates
.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.

Value

A tibble of randomly generated data.

Details

This function uses the underlying stats::rexp(), and its underlying p, d, and q functions. For more information please see stats::rexp()

Other Continuous Distribution: tidy_beta(), tidy_burr(), tidy_cauchy(), tidy_chisquare(), 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_triangular(), tidy_uniform(), tidy_weibull(), tidy_zero_truncated_geometric()

Other Exponential: tidy_inverse_exponential(), util_exponential_param_estimate(), util_exponential_stats_tbl()

Author

Steven P. Sanderson II, MPH

Examples

tidy_exponential()
#> # A tibble: 50 × 7
#>    sim_number     x     y       dx      dy     p     q
#>    <fct>      <int> <dbl>    <dbl>   <dbl> <dbl> <dbl>
#>  1 1              1 0.212 -0.889   0.00160 0.191 0.212
#>  2 1              2 0.688 -0.761   0.00575 0.497 0.688
#>  3 1              3 0.724 -0.634   0.0176  0.515 0.724
#>  4 1              4 0.173 -0.507   0.0457  0.159 0.173
#>  5 1              5 3.40  -0.380   0.102   0.967 3.40 
#>  6 1              6 2.13  -0.253   0.194   0.881 2.13 
#>  7 1              7 0.812 -0.125   0.319   0.556 0.812
#>  8 1              8 0.265  0.00190 0.453   0.232 0.265
#>  9 1              9 1.12   0.129   0.564   0.674 1.12 
#> 10 1             10 0.762  0.256   0.624   0.533 0.762
#> # ℹ 40 more rows