blorr (version 0.3.0)

blr_roc_curve: ROC curve

Description

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

Usage

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.

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()

Examples

# NOT RUN {
model <- glm(honcomp ~ female + read + science, data = hsb2,
             family = binomial(link = 'logit'))
k <- blr_gains_table(model)
blr_roc_curve(k)

# }