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Plot Method for obwoe Objects

Source: R/obwoe.R
plot.obwoe.Rd

Creates publication-quality visualizations of optimal binning results. Supports multiple plot types including IV ranking charts, WoE profiles, and bin distribution plots. All plots follow credit scoring visualization conventions.

Usage

# S3 method for class 'obwoe'
plot(
  x,
  type = c("iv", "woe", "bins"),
  feature = NULL,
  top_n = 15,
  show_threshold = TRUE,
  ...
)

Arguments

x

An object of class "obwoe".

type

Character string specifying the plot type:

"iv"

Information Value ranking bar chart (default)

"woe"

Weight of Evidence profile for selected features

"bins"

Bin distribution (count and event rate)

feature

Character vector of feature names to plot (for "woe" and "bins" types). If NULL, uses top 6 features by IV.

top_n

Integer. For "iv" type, number of top features to display. Default is 15. Set to NULL to display all.

show_threshold

Logical. For "iv" type, draw horizontal lines at IV thresholds (0.02, 0.10, 0.30)? Default is TRUE.

...

Additional arguments passed to base plotting functions.

Value

Invisibly returns NULL. Called for side effect (plotting).

Details

Plot Types

IV Ranking (type = "iv"): Horizontal bar chart showing features ranked by Information Value. Colors indicate predictive power classification:

  • Gray: IV < 0.02 (Unpredictive)

  • Yellow: 0.02 <= IV < 0.10 (Weak)

  • Orange: 0.10 <= IV < 0.30 (Medium)

  • Green: 0.30 <= IV < 0.50 (Strong)

  • Red: IV >= 0.50 (Suspicious)

WoE Profile (type = "woe"): Bar chart showing Weight of Evidence values for each bin. Positive WoE indicates higher-than-average event rate; negative WoE indicates lower-than-average event rate. Monotonic WoE patterns are generally preferred for interpretability.

Bin Distribution (type = "bins"): Dual-axis plot showing observation counts (bars) and event rates (line). Useful for diagnosing bin quality and class imbalance.

References

Thomas, L. C., Edelman, D. B., & Crook, J. N. (2002). Credit Scoring and Its Applications. SIAM Monographs on Mathematical Modeling and Computation. doi:10.1137/1.9780898718317

See also

obwoe, summary.obwoe.

Examples

# \donttest{
set.seed(42)
df <- data.frame(
  age = rnorm(500, 40, 15),
  income = rgamma(500, 2, 0.0001),
  score = rnorm(500, 600, 100),
  target = rbinom(500, 1, 0.2)
)
result <- obwoe(df, target = "target")

# IV ranking chart
plot(result, type = "iv")


# WoE profile for specific feature
plot(result, type = "woe", feature = "age")


# Bin distribution
plot(result, type = "bins", feature = "income")

# }