Receiver operating characteristic curve (ROC) curve is used for assessing accuracy of the model classification. It depicts sensitivity on the Y axis and 1 – specificity on the X axis.

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)

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.

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

Examples

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