smbinning.custom: Customized Binning

In smbinning: Scoring Modeling and Optimal Binning

Description

It gives the user the ability to create customized cutpoints.

Usage

smbinning.custom(df, y, x, cuts)

Arguments

df	A data frame.
y	Binary response variable (0,1). Integer (int) is required. Name of y must not have a dot. Name "default" is not allowed.
x	Continuous characteristic. At least 5 different values. Value Inf is not allowed. Name of x must not have a dot.
cuts	Vector with the cutpoints selected by the user. It does not have a default so user must define it.

Value

The command smbinning.custom generates and object containing the necessary info and utilities for binning. The user should save the output result so it can be used with smbinning.plot, smbinning.sql, and smbinning.gen.

Examples

# Load library and its dataset
library(smbinning) # Load package and its data

# Custom cutpoints using percentiles (20% each)
cbs1cuts=as.vector(quantile(smbsimdf1$cbs1, probs=seq(0,1,0.2), na.rm=TRUE)) # Quantiles
cbs1cuts=cbs1cuts[2:(length(cbs1cuts)-1)] # Remove first (min) and last (max) values

# Example: Customized binning
result=smbinning.custom(df=smbsimdf1,y="fgood",x="cbs1",cuts=cbs1cuts) # Run and save
result$ivtable # Tabulation and Information Value

Example output

Loading required package: sqldf
Loading required package: gsubfn
Loading required package: proto
Loading required package: RSQLite
Loading required package: partykit
Loading required package: grid
Loading required package: libcoin
Loading required package: mvtnorm
Loading required package: Formula
Warning message:
no DISPLAY variable so Tk is not available 
   Cutpoint CntRec CntGood CntBad CntCumRec CntCumGood CntCumBad PctRec
1 <= 41.954    449     285    164       449        285       164 0.1796
2 <= 49.366    449     335    114       898        620       278 0.1796
3 <= 55.588    448     359     89      1346        979       367 0.1792
4 <= 63.212    449     393     56      1795       1372       423 0.1796
5  > 63.212    449     423     26      2244       1795       449 0.1796
6   Missing    256     205     51      2500       2000       500 0.1024
7     Total   2500    2000    500        NA         NA        NA 1.0000
  GoodRate BadRate    Odds LnOdds     WoE     IV
1   0.6347  0.3653  1.7378 0.5526 -0.8337 0.1547
2   0.7461  0.2539  2.9386 1.0779 -0.3084 0.0187
3   0.8013  0.1987  4.0337 1.3947  0.0084 0.0000
4   0.8753  0.1247  7.0179 1.9485  0.5622 0.0475
5   0.9421  0.0579 16.2692 2.7893  1.4030 0.2238
6   0.8008  0.1992  4.0196 1.3912  0.0049 0.0000
7   0.8000  0.2000  4.0000 1.3863  0.0000 0.4447

smbinning documentation built on May 1, 2019, 10:06 p.m.