boots.vld: Bootstrap model validation

In PDtoolkit: Collection of Tools for PD Rating Model Development and Validation

Bootstrap model validation

Description

boots.vld performs bootstrap model validation. The goal of this procedure is to generate main model performance metrics such as absolute mean square error, root mean square error or area under curve (AUC) based on resampling method.

Usage

boots.vld(model, B = 1000, seed = 1122)

Arguments

model	Model in use, an object of class inheriting from "glm".
B	Number of bootstrap samples. Default is set to 1000.
seed	Random seed needed for ensuring the result reproducibility. Default is 1122.

Value

The command boots.vld returns a list of two objects.
The first object (iter), returns iteration performance metrics.
The second object (summary), is the data frame of iterations averages of performance metrics.

Examples

suppressMessages(library(PDtoolkit))
data(loans)
#run stepFWD
res <- stepFWD(start.model = Creditability ~ 1, 
                p.value = 0.05, 
	   coding = "WoE",
	   db = loans)
#check output elements
names(res)
#extract the final model
final.model <- res$model
#print coefficients
summary(final.model)$coefficients
#print head of coded development data
head(res$dev.db)
#calculate AUC
auc.model(predictions = predict(final.model, type = "response", newdata = res$dev.db),
    observed = res$dev.db$Creditability)
boots.vld (model = final.model, B = 10, seed = 1122)

PDtoolkit documentation built on Sept. 20, 2023, 9:06 a.m.