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)

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. |

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)