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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  1.06   -3.22 0.000492 0.856   1.06   Gaussian c(0, 1)
#>  2 1              2  0.826  -3.10 0.00147  0.796   0.826  Gaussian c(0, 1)
#>  3 1              3  0.0471 -2.99 0.00382  0.519   0.0471 Gaussian c(0, 1)
#>  4 1              4 -1.02   -2.87 0.00865  0.154  -1.02   Gaussian c(0, 1)
#>  5 1              5 -1.60   -2.76 0.0171   0.0551 -1.60   Gaussian c(0, 1)
#>  6 1              6 -0.238  -2.65 0.0295   0.406  -0.238  Gaussian c(0, 1)
#>  7 1              7  0.0404 -2.53 0.0447   0.516   0.0404 Gaussian c(0, 1)
#>  8 1              8  0.382  -2.42 0.0600   0.649   0.382  Gaussian c(0, 1)
#>  9 1              9 -0.884  -2.30 0.0719   0.188  -0.884  Gaussian c(0, 1)
#> 10 1             10  0.548  -2.19 0.0783   0.708   0.548  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 -0.972  -3.47 0.000324 0.165  -0.972  Gaussian c(0, 1)
#>  2 1              2  0.660  -3.33 0.000853 0.745   0.660  Gaussian c(0, 1)
#>  3 1              3 -2.13   -3.20 0.00202  0.0166 -2.13   Gaussian c(0, 1)
#>  4 1              4  0.616  -3.06 0.00432  0.731   0.616  Gaussian c(0, 1)
#>  5 1              5 -0.0514 -2.92 0.00836  0.480  -0.0514 Gaussian c(0, 1)
#>  6 1              6  0.443  -2.78 0.0147   0.671   0.443  Gaussian c(0, 1)
#>  7 1              7 -0.198  -2.65 0.0237   0.422  -0.198  Gaussian c(0, 1)
#>  8 1              8 -1.20   -2.51 0.0352   0.116  -1.20   Gaussian c(0, 1)
#>  9 1              9 -0.966  -2.37 0.0492   0.167  -0.966  Gaussian c(0, 1)
#> 10 1             10 -1.34   -2.24 0.0654   0.0894 -1.34   Gaussian c(0, 1)
#> # ℹ 190 more rows