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 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,
  ...
)

Arguments

model

An object of class glm.

data

A tibble or a data.frame.

x

An object of class blr_gains_table.

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 TRUE, prints the plot else returns a plot object.

...

Other inputs.

Value

A tibble.

References

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.

See also

Examples

model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) # gains table blr_gains_table(model)
#> decile total 1 0 ks tp tn fp fn sensitivity specificity accuracy #> 1 1 20 14 6 22.33346 14 141 6 39 26.41509 95.91837 77.5 #> 2 2 20 13 7 42.09986 27 134 13 26 50.94340 91.15646 80.5 #> 3 3 20 10 10 54.16506 37 124 23 16 69.81132 84.35374 80.5 #> 4 4 20 7 13 58.52907 44 111 36 9 83.01887 75.51020 77.5 #> 5 5 20 3 17 52.62482 47 94 53 6 88.67925 63.94558 70.5 #> 6 6 20 3 17 46.72058 50 77 70 3 94.33962 52.38095 63.5 #> 7 7 20 1 19 35.68220 51 58 89 2 96.22642 39.45578 54.5 #> 8 8 20 2 18 27.21088 53 40 107 0 100.00000 27.21088 46.5 #> 9 9 20 0 20 13.60544 53 20 127 0 100.00000 13.60544 36.5 #> 10 10 20 0 20 0.00000 53 0 147 0 100.00000 0.00000 26.5
# lift chart k <- blr_gains_table(model) plot(k)