update readme and demo file

Author: xxzcool

README.md

@@ -18,7 +18,7 @@ This package depends on 'smbinning' prior to version 0.4, so you have to install
 
 
 ## Usage
-The following is a demo for illustrating how to use this package to establish a credit scoring model from beginning to end. You can run the following codes manually, or use 'demo(scoremodel,package="scoremodel")' directly.
+The following is a demo for illustrating how to use this package to establish a credit scoring model from beginning to end. You can run the following codes manually, or use demo(scoremodel,package="scoremodel") directly.
 
 ```R
 library(scoremodel)

demo/scoremodel.R

@@ -2,42 +2,53 @@
 ## Demo file for scoremodel; start with 'demo(scoremodel)'
 ## -----------------------------------------------------------------------------------
 
-devAskNewPage(ask	=	FALSE)
+devAskNewPage(ask = FALSE)
 library(scoremodel)
 data(CreditData)
 #convert character variables to factors
+
 mysample <- convertType(CreditData, toType = "fac", vars = -1)
 #homogeneity exclusion
+
 mysample <- delNArate(mysample, narate_critical = 0.9)[[3]]
 mysample <- delFewValues(mysample, minN = 5, minF = 2, exclude = "target")
 mysample <- delSinvalPercent(mysample, percent = 0.9, exclude = "target")
 #split dataset to train and test
+
 set.seed(21)
 splitresult <- splitData(mysample, size = 0.7, ifpercent = TRUE)
 train <- splitresult[[1]]
 test <- splitresult[[2]]
 #optimal binning and generate woe configuration list
+
 configlist <- dfBinningFun(train, binMethod = 1, p = 0.05, maxcat = 10, aliquots = 5)
 #woe encoding
+
 x_train_encoding <- woeEncodeFun_df(train[-length(train)], config_list = configlist)
 x_test_encoding <- woeEncodeFun_df(test[-length(test)], config_list = configlist)
 train_encoding <- cbind(x_train_encoding, train["target"])
 #collinear elimination
+
 train_encoding_red <- collElimination(train_encoding, cor_critical = 0.8)
 #model training
+
 fit <- LRfit(train_encoding_red, sig = 0.05)
 summary(fit)
 #prediction
+
 p_train <- LRpredict(fit)
 p_test <- LRpredict(fit, newdata = x_test_encoding)
 #calculate KS of model
+
 myks(train[,"target"], p_train)
 myks(test[,"target"], p_test)
 #calculate PSI of model
+
 psi(p_train, p_test, bins = 10, binMethod = "EF")
 #model performance visualization
+
 myCurves(train[,"target"], p_train, test[,"target"], p_test, ontest = FALSE,
          lift_bins = 10, P0 = 600, PDO = 50, color_scheme = 1, ifsave = FALSE)
 myCurves(train[,"target"], p_train, test[,"target"], p_test, ontest = TRUE,
          lift_bins = 10, P0 = 600, PDO = 50, color_scheme = 1, ifsave = FALSE)
-devAskNewPage(ask	=	TRUE)
+devAskNewPage(ask = TRUE)