Weight of evidence and information value. Currently avialable for categorical predictors only.

blr_woe_iv(data, predictor, response, digits = 4, ...)

# S3 method for blr_woe_iv
plot(
  x,
  title = NA,
  xaxis_title = "Levels",
  yaxis_title = "WoE",
  bar_color = "blue",
  line_color = "red",
  print_plot = TRUE,
  ...
)

Arguments

data

A tibble or data.frame.

predictor

Predictor variable; column in data.

response

Response variable; column in data.

digits

Number of decimal digits to round off.

...

Other inputs.

x

An object of class blr_segment_dist.

title

Plot title.

xaxis_title

X axis title.

yaxis_title

Y axis title.

bar_color

Color of the bar.

line_color

Color of the horizontal line.

print_plot

logical; if TRUE, prints the plot else returns a plot object.

Value

A tibble.

References

Siddiqi N (2006): Credit Risk Scorecards: developing and implementing intelligent credit scoring. New Jersey, Wiley.

See also

Examples

# woe and iv
k <- blr_woe_iv(hsb2, female, honcomp)
k
#>                            Weight of Evidence                             
#> -------------------------------------------------------------------------
#> levels    count_0s    count_1s    dist_0s    dist_1s        woe      iv   
#> -------------------------------------------------------------------------
#>   0          73          18          0.50       0.34       0.38     0.06  
#>   1          74          35          0.50       0.66      -0.27     0.04  
#> -------------------------------------------------------------------------
#> 
#>       Information Value       
#> -----------------------------
#> Variable    Information Value 
#> -----------------------------
#>  female          0.1023       
#> -----------------------------

# plot woe
plot(k)