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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 -1.53   -4.16 0.000278 0.0632   -1.53   Gaussian c(0, 1)
#>  2 1              2 -3.19   -4.00 0.00108  0.000711 -3.19   Gaussian c(0, 1)
#>  3 1              3 -0.318  -3.84 0.00328  0.375    -0.318  Gaussian c(0, 1)
#>  4 1              4 -0.268  -3.68 0.00784  0.394    -0.268  Gaussian c(0, 1)
#>  5 1              5  2.69   -3.52 0.0147   0.996     2.69   Gaussian c(0, 1)
#>  6 1              6 -0.0990 -3.36 0.0215   0.461    -0.0990 Gaussian c(0, 1)
#>  7 1              7  0.891  -3.20 0.0247   0.813     0.891  Gaussian c(0, 1)
#>  8 1              8  0.382  -3.04 0.0222   0.649     0.382  Gaussian c(0, 1)
#>  9 1              9  0.867  -2.88 0.0157   0.807     0.867  Gaussian c(0, 1)
#> 10 1             10 -0.772  -2.72 0.00873  0.220    -0.772  Gaussian c(0, 1)
#> # ℹ 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  1.66   -3.59 0.000237 0.952    1.66   Gaussian c(0, 1)
#>  2 1              2 -2.45   -3.46 0.000628 0.00707 -2.45   Gaussian c(0, 1)
#>  3 1              3 -0.0812 -3.33 0.00148  0.468   -0.0812 Gaussian c(0, 1)
#>  4 1              4  0.788  -3.20 0.00308  0.785    0.788  Gaussian c(0, 1)
#>  5 1              5 -0.959  -3.07 0.00573  0.169   -0.959  Gaussian c(0, 1)
#>  6 1              6  0.916  -2.94 0.00947  0.820    0.916  Gaussian c(0, 1)
#>  7 1              7 -1.08   -2.81 0.0140   0.140   -1.08   Gaussian c(0, 1)
#>  8 1              8 -0.0673 -2.68 0.0186   0.473   -0.0673 Gaussian c(0, 1)
#>  9 1              9 -0.301  -2.54 0.0223   0.382   -0.301  Gaussian c(0, 1)
#> 10 1             10 -0.321  -2.41 0.0248   0.374   -0.321  Gaussian c(0, 1)
#> # ℹ 190 more rows