A tidyverse friendly way to plug WoE versions of a set of predictor variables against a given binary outcome.
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 likestarts_with()andmatches().- dictionary
A tbl. If NULL the function will build a dictionary with those variables passed to
.... You can pass a custom dictionary too, seedictionary()for details.- prefix
A character string that will be the prefix to the resulting new variables.
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 × 14
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <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
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # ℹ 22 more rows
#> # ℹ 3 more variables: woe_cyl <dbl>, woe_gear <dbl>, woe_carb <dbl>