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This allows a user to specify any n number of tidy_ distributions that can be combined into a single tibble. This is the preferred method for combining multiple distributions of different types, for example a Gaussian distribution and a Beta distribution.

This generates a single tibble with an added column of dist_type that will give the distribution family name and its associated parameters.

Usage

tidy_combine_distributions(...)

Arguments

...

The ... is where you can place your different distributions

Value

A tibble

Details

Allows a user to generate a tibble of different tidy_ distributions

See also

Other Multiple Distribution: tidy_multi_single_dist()

Author

Steven P. Sanderson II, MPH

Examples


tn <- tidy_normal()
tb <- tidy_beta()
tc <- tidy_cauchy()

tidy_combine_distributions(tn, tb, tc)
#> # A tibble: 150 × 8
#>    sim_number     x       y    dx       dy       p       q dist_type       
#>    <fct>      <int>   <dbl> <dbl>    <dbl>   <dbl>   <dbl> <fct>           
#>  1 1              1 -2.71   -3.60 0.000353 0.00337 -2.71   Gaussian c(0, 1)
#>  2 1              2 -0.109  -3.46 0.00134  0.456   -0.109  Gaussian c(0, 1)
#>  3 1              3  0.245  -3.32 0.00416  0.597    0.245  Gaussian c(0, 1)
#>  4 1              4  1.79   -3.18 0.0107   0.963    1.79   Gaussian c(0, 1)
#>  5 1              5  0.338  -3.05 0.0226   0.632    0.338  Gaussian c(0, 1)
#>  6 1              6 -0.805  -2.91 0.0397   0.211   -0.805  Gaussian c(0, 1)
#>  7 1              7 -0.829  -2.77 0.0583   0.204   -0.829  Gaussian c(0, 1)
#>  8 1              8  1.37   -2.63 0.0716   0.915    1.37   Gaussian c(0, 1)
#>  9 1              9 -0.0993 -2.50 0.0737   0.460   -0.0993 Gaussian c(0, 1)
#> 10 1             10 -1.18   -2.36 0.0636   0.119   -1.18   Gaussian c(0, 1)
#> # … with 140 more rows

## OR

tidy_combine_distributions(
  tidy_normal(),
  tidy_beta(),
  tidy_cauchy(),
  tidy_logistic()
)
#> # A tibble: 200 × 8
#>    sim_number     x       y    dx       dy     p       q dist_type       
#>    <fct>      <int>   <dbl> <dbl>    <dbl> <dbl>   <dbl> <fct>           
#>  1 1              1  2.28   -3.23 0.000450 0.989  2.28   Gaussian c(0, 1)
#>  2 1              2  1.74   -3.09 0.00117  0.959  1.74   Gaussian c(0, 1)
#>  3 1              3  0.346  -2.95 0.00272  0.635  0.346  Gaussian c(0, 1)
#>  4 1              4  0.0875 -2.82 0.00568  0.535  0.0875 Gaussian c(0, 1)
#>  5 1              5  0.719  -2.68 0.0107   0.764  0.719  Gaussian c(0, 1)
#>  6 1              6 -0.970  -2.54 0.0181   0.166 -0.970  Gaussian c(0, 1)
#>  7 1              7  0.932  -2.40 0.0277   0.824  0.932  Gaussian c(0, 1)
#>  8 1              8 -0.519  -2.26 0.0384   0.302 -0.519  Gaussian c(0, 1)
#>  9 1              9 -0.407  -2.13 0.0482   0.342 -0.407  Gaussian c(0, 1)
#> 10 1             10 -0.425  -1.99 0.0556   0.336 -0.425  Gaussian c(0, 1)
#> # … with 190 more rows