Gains table & lift chart
Compute sensitivity, specificity, accuracy and KS statistics to generate the lift chart and the KS chart.
blr_gains_table(model, data = NULL) ## S3 method for class 'blr_gains_table' plot( x, title = "Lift Chart", xaxis_title = "% Population", yaxis_title = "% Cumulative 1s", diag_line_col = "red", lift_curve_col = "blue", plot_title_justify = 0.5, print_plot = TRUE, ... )
model |
An object of class |
data |
A |
x |
An object of class |
title |
Plot title. |
xaxis_title |
X axis title. |
yaxis_title |
Y axis title. |
diag_line_col |
Diagonal line color. |
lift_curve_col |
Color of the lift curve. |
plot_title_justify |
Horizontal justification on the plot title. |
print_plot |
logical; if |
... |
Other inputs. |
A tibble.
Agresti, A. (2007), An Introduction to Categorical Data Analysis, Second Edition, New York: John Wiley & Sons.
Agresti, A. (2013), Categorical Data Analysis, Third Edition, New York: John Wiley & Sons.
Thomas LC (2009): Consumer Credit Models: Pricing, Profit, and Portfolio. Oxford, Oxford Uni-versity Press.
Sobehart J, Keenan S, Stein R (2000): Benchmarking Quantitative Default Risk Models: A Validation Methodology, Moody’s Investors Service.
Other model validation techniques:
blr_confusion_matrix(),
blr_decile_capture_rate(),
blr_decile_lift_chart(),
blr_gini_index(),
blr_ks_chart(),
blr_lorenz_curve(),
blr_roc_curve(),
blr_test_hosmer_lemeshow()
model <- glm(honcomp ~ female + read + science, data = hsb2,
family = binomial(link = 'logit'))
# gains table
blr_gains_table(model)
# lift chart
k <- blr_gains_table(model)
plot(k)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.