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 )
gains_table | An object of class |
---|---|
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 |
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.
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()
model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) k <- blr_gains_table(model) blr_roc_curve(k)