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, ... )
data | A |
---|---|
predictor | Predictor variable; column in |
response | Response variable; column in |
digits | Number of decimal digits to round off. |
... | Other inputs. |
x | An object of class |
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 |
A tibble.
Siddiqi N (2006): Credit Risk Scorecards: developing and implementing intelligent credit scoring. New Jersey, Wiley.
Other bivariate analysis procedures:
blr_bivariate_analysis()
,
blr_segment_dist()
,
blr_segment_twoway()
,
blr_segment()
,
blr_woe_iv_stats()
# 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)