install.packages("devtools", dependencies = TRUE) 

library(devtools)
devtools::install_github("ayhandis/creditR")
library(creditR)

ls("package:creditR")

 [1] "Adjusted.Binomial.test"              "Adjusted.Herfindahl.Hirschman.Index" "Anchor.point"                       
 [4] "bayesian.calibration"                "Binomial.test"                       "chisquare.test"                     
 [7] "correlation.cluster"                 "Gini.univariate"                     "Gini.univariate.data"               
[10] "Gini_elimination"                    "Herfindahl.Hirschman.Index"          "IV.calc"                            
[13] "IV.calc.data"                        "IV_elimination"                      "k.fold.cross.validation.glm"        
[16] "Kolmogorov.Smirnov"                  "master.scale"                        "missing_elimination"                
[19] "missing_ratio"                       "na_checker"                          "na_filler_contvar"                  
[22] "PSI.calc"                            "PSI.calc.data"                       "regression.calibration"             
[25] "scaled.score"                        "SSI.calc"                            "SSI.calc.data"                      
[28] "train_test_balanced_split"           "train_test_split"                    "variable.clustering"                
[31] "variable.clustering.gini"            "vif.calc"                            "woe.get.clear.data"                 
[34] "woe.glm.feature.importance"          "woe.table.calc"                     

#This R script is designed to make the creditR package easier to understand.
#Obtaining a high accuracy model is not within the scope of this study.

#Attaching the library
library(creditR)

#Model data and data structure
data("germancredit")
str(germancredit)

#Preparing a sample data set.
sample_data <- germancredit[,c("duration.in.month","credit.amount","installment.rate.in.percentage.of.disposable.income",
                               "age.in.years","creditability")]

#Converting the ‘Creditability’ (default flag) variable into numeric type.
sample_data$creditability <- ifelse(sample_data$creditability == "bad",1,0)

#Calculating the missing ratios.
missing_ratio(sample_data)

#Splitting the data into train and test sets.
traintest <- train_test_split(sample_data,123,0.70)
train <- traintest$train
test <- traintest$test

#Applying WOE transformation on the variables.
woerules <- woe.binning(df = train,target.var = "creditability",pred.var = train,event.class = 1)
train_woe <- woe.binning.deploy(train, woerules, add.woe.or.dum.var='woe')

#Creating a dataset with the transformed variables and default flag.
train_woe <- woe.get.clear.data(train_woe,default_flag = "creditability",prefix = "woe")

#Applying the WOE rules used on the train data to the test data.
test_woe <- woe.binning.deploy(test, woerules, add.woe.or.dum.var='woe')
test_woe <- woe.get.clear.data(test_woe,default_flag = "creditability",prefix = "woe")

#Performing the IV and Gini calculations for the whole data set.
IV.calc.data(train_woe,"creditability")
Gini.univariate.data(train_woe,"creditability")

#Creating a new dataset by Gini elimination. IV elimination is also possible.
eliminated_data <- Gini_elimination(train_woe,"creditability",0.10)
str(eliminated_data)

#A demonstration of the functions useful in performing Clustering.
clustering_data <- variable.clustering(eliminated_data,"creditability", 2)
variable.clustering.gini(eliminated_data,"creditability", 2) # Returns the data for variables that have the maximum gini value in the dataset.
correlation.cluster(eliminated_data,clustering_data,variables = "variable",clusters = "Group")

#Creating a logistic regression model of the data.
model= glm(formula = creditability ~ .,
           family = binomial(link = "logit"),
           data = eliminated_data)
summary(model)

#Calculating variable weights. 
woe.glm.feature.importance(eliminated_data,model,"creditability")

#Generating the PD values for the train and test data.
ms_train_data <- cbind(eliminated_data,model$fitted.values)
ms_test_data <- cbind(test_woe[,colnames(eliminated_data)], predict(model,type = "response",newdata = test_woe))
colnames(ms_train_data) <- c("woe.duration.in.month.binned","woe.age.in.years.binned","woe.installment.rate.in.percentage.of.disposable.income.binned","creditability","PD")
colnames(ms_test_data) <- c("woe.duration.in.month.binned","woe.age.in.years.binned","woe.installment.rate.in.percentage.of.disposable.income.binned","creditability","PD")

#An example application of the Regression calibration method. The model is calibrated to the test_woe data.
regression.calibration(model,test_woe,"creditability")

#Creating a master scale.
master_scale <- master.scale(ms_train_data,"creditability","PD")


#Calibrating the master scale and the modeling data to the default rate of 5% using the bayesian calibration method.
ms_train_data$Score = log(ms_train_data$PD/(1-ms_train_data$PD)) 
ms_test_data$Score = log(ms_test_data$PD/(1-ms_test_data$PD)) 
bayesian_method <- bayesian.calibration(data = master_scale,average_score ="Score",total_observations = "Total.Observations",PD = "PD",central_tendency = 0.05,calibration_data = ms_train_data,calibration_data_score ="Score")

#After calibration, the information and data related to the calibration process can be obtained as follows.
bayesian_method$Calibration.model
bayesian_method$Calibration.formula
bayesian_method$data
bayesian_method$calibration_data

#The Scaled score can be created using the following function.
scaled.score(bayesian_method$calibration_data, "calibrated_pd", 3000, 15)

#Note : Model calibration is performed for illustration only. Model validation tests proceed through the original master scale as follows.

#Calculating the Vif values of the variables.
vif.calc(model)

#Calculating the Gini for the model.
Gini(model$fitted.values,ms_train_data$creditability)

#Performing the 5 Fold cross validation.
k.fold.cross.validation.glm(ms_train_data,"creditability",5,1)

#The KS test is performed on the distributions of the estimates for good and bad observations.
Kolmogorov.Smirnov(ms_train_data,"creditability","PD")
Kolmogorov.Smirnov(ms_test_data,"creditability","PD")

#Variable stabilities are measured.
SSI.calc.data(train_woe,test_woe,"creditability")

#The HHI test is performed to measure the concentration of the master scale.
Herfindahl.Hirschman.Index(master_scale,"Total.Observations")

#Performing the Anchor point test.
Anchor.point(master_scale,"PD","Total.Observations",0.30)

#The Chi-square test is applied on the master scale.
chisquare.test(master_scale,"PD","Bad.Count","Total.Observations",0.90)

#The Binomial test is applied on the master scale.
master_scale$DR <- master_scale$Bad.Count/master_scale$Total.Observations
Binomial.test(master_scale,"Total.Observations","PD","DR",0.90,"one")