Combine Multiple Tidy Distributions of Different Types — tidy_combine_distributions • TidyDensity
Description: 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.

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# Combine Multiple Tidy Distributions of Different Types

Source: `R/combine-tidy-distributions-tbl.R`
`tidy_combine_distributions.Rd`

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 -0.631  -4.24 0.000239 0.264   -0.631  Gaussian c(0, 1)
    #>  2 1              2  0.231  -4.07 0.000741 0.591    0.231  Gaussian c(0, 1)
    #>  3 1              3 -0.815  -3.91 0.00197  0.207   -0.815  Gaussian c(0, 1)
    #>  4 1              4 -1.17   -3.75 0.00455  0.121   -1.17   Gaussian c(0, 1)
    #>  5 1              5 -0.0895 -3.59 0.00910  0.464   -0.0895 Gaussian c(0, 1)
    #>  6 1              6 -0.202  -3.42 0.0158   0.420   -0.202  Gaussian c(0, 1)
    #>  7 1              7 -2.72   -3.26 0.0238   0.00331 -2.72   Gaussian c(0, 1)
    #>  8 1              8 -1.17   -3.10 0.0313   0.122   -1.17   Gaussian c(0, 1)
    #>  9 1              9 -1.16   -2.94 0.0361   0.123   -1.16   Gaussian c(0, 1)
    #> 10 1             10 -0.730  -2.77 0.0367   0.233   -0.730  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  1.07   -3.28 0.000211 0.858   1.07   Gaussian c(0, 1)
    #>  2 1              2  0.244  -3.11 0.000621 0.596   0.244  Gaussian c(0, 1)
    #>  3 1              3 -0.397  -2.95 0.00160  0.346  -0.397  Gaussian c(0, 1)
    #>  4 1              4  1.44   -2.78 0.00363  0.925   1.44   Gaussian c(0, 1)
    #>  5 1              5 -0.867  -2.62 0.00735  0.193  -0.867  Gaussian c(0, 1)
    #>  6 1              6  2.17   -2.45 0.0135   0.985   2.17   Gaussian c(0, 1)
    #>  7 1              7 -0.270  -2.28 0.0228   0.394  -0.270  Gaussian c(0, 1)
    #>  8 1              8  0.0824 -2.12 0.0367   0.533   0.0824 Gaussian c(0, 1)
    #>  9 1              9 -0.748  -1.95 0.0567   0.227  -0.748  Gaussian c(0, 1)
    #> 10 1             10 -1.50   -1.79 0.0853   0.0666 -1.50   Gaussian c(0, 1)
    #> # ℹ 190 more rows