Add WoE in a data frame

A tidyverse friendly way to plug WoE versions of a set of predictor variables against a given binary outcome.

add_woe(.data, outcome, ..., dictionary = NULL, prefix = "woe")

Arguments

.data	A tbl. The data.frame to plug the new woe version columns.
outcome	The bare name of the outcome variable.
...	Bare names of predictor variables, passed as you would pass variables to dplyr::select(). This means that you can use all the helpers like starts_with() and matches().
dictionary	A tbl. If NULL the function will build a dictionary with those variables passed to .... You can pass a custom dictionary too, see dictionary() for details.
prefix	A character string that will be the prefix to the resulting new variables.

Value

A tibble with the original columns of .data plus the woe columns wanted.

Details

You can pass a custom dictionary to add_woe(). It must have the exactly the same structure of the output of dictionary(). One easy way to do this is to tweak a output returned from it.

Examples

mtcars %>% add_woe("am", cyl, gear:carb)
#> # A tibble: 32 x 14
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb woe_cyl
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl>
#>  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4  0.0918
#>  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4  0.0918
#>  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1  1.36  
#>  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1  0.0918
#>  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2 -1.41  
#>  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1  0.0918
#>  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4 -1.41  
#>  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2  1.36  
#>  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2  1.36  
#> 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4  0.0918
#> # … with 22 more rows, and 2 more variables: woe_gear <dbl>, woe_carb <dbl>