blr_roc_curve function

ROC curve

Receiver operating characteristic curve (ROC) curve is used for assessing accuracy of the model classification.

blr_roc_curve(
  gains_table,
  title = "ROC Curve",
  xaxis_title = "1 - Specificity",
  yaxis_title = "Sensitivity",
  roc_curve_col = "blue",
  diag_line_col = "red",
  point_shape = 18,
  point_fill = "blue",
  point_color = "blue",
  plot_title_justify = 0.5,
  print_plot = TRUE
)

Arguments

• gains_table: An object of class blr_gains_table.
• title: Plot title.
• xaxis_title: X axis title.
• yaxis_title: Y axis title.
• roc_curve_col: Color of the roc curve.
• diag_line_col: Diagonal line color.
• point_shape: Shape of the points on the roc curve.
• point_fill: Fill of the points on the roc curve.
• point_color: Color of the points on the roc curve.
• plot_title_justify: Horizontal justification on the plot title.
• print_plot: logical; if TRUE, prints the plot else returns a plot object.

Examples

model <- glm(honcomp ~ female + read + science, data = hsb2,
             family = binomial(link = 'logit'))
k <- blr_gains_table(model)
blr_roc_curve(k)

References

Agresti, A. (2007), An Introduction to Categorical Data Analysis, Second Edition, New York: John Wiley & Sons.

Hosmer, D. W., Jr. and Lemeshow, S. (2000), Applied Logistic Regression, 2nd Edition, New York: John Wiley & Sons.

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

Thomas LC, Edelman DB, Crook JN (2002): Credit Scoring and Its Applications. Philadelphia, SIAM Monographs on Mathematical Modeling and Computation.

See Also

Other model validation techniques: blr_confusion_matrix(), blr_decile_capture_rate(), blr_decile_lift_chart(), blr_gains_table(), blr_gini_index(), blr_ks_chart(), blr_lorenz_curve(), blr_test_hosmer_lemeshow()