smbinning.scaling {smbinning}

Scaling

Description

It transforms the coefficients of a logistic regression into scaled points based on the following three parameters pre-selected by the analyst: PDO, Score, and Odds.

Usage

smbinning.scaling(logitraw, pdo = 20, score = 720, odds = 99)

Arguments

logitraw

Logistic regression (glm) that must have specified family=binomial and whose variables have been generated with smbinning.gen or smbinning.factor.gen.

pdo

Points to double the oods.

score

Score at which the desire odds occur.

odds

Desired odds at the selected score.

Value

A scaled model from a logistic regression built with binned variables, the parameters used in the scaling process, the expected minimum and maximum score, and the original logistic model.

Examples

# Load library and its dataset
library(smbinning)

# Sampling
pop=smbsimdf1 # Population
train=subset(pop,rnd<=0.7) # Training sample

# Generate binning object to generate variables
smbcbs1=smbinning(train,x="cbs1",y="fgood")
smbcbinq=smbinning.factor(train,x="cbinq",y="fgood")
smbcblineut=smbinning.custom(train,x="cblineut",y="fgood",cuts=c(30,40,50))
smbpmt=smbinning.factor(train,x="pmt",y="fgood")
smbtob=smbinning.custom(train,x="tob",y="fgood",cuts=c(1,2,3))
smbdpd=smbinning.factor(train,x="dpd",y="fgood")
smbdep=smbinning.custom(train,x="dep",y="fgood",cuts=c(10000,12000,15000))
smbod=smbinning.factor(train,x="od",y="fgood")
smbhome=smbinning.factor(train,x="home",y="fgood")
smbinc=smbinning.factor.custom(
  train,x="inc",y="fgood",
  c("'W01','W02'","'W03','W04','W05'","'W06','W07'","'W08','W09','W10'"))

pop=smbinning.gen(pop,smbcbs1,"g1cbs1")
pop=smbinning.factor.gen(pop,smbcbinq,"g1cbinq")
pop=smbinning.gen(pop,smbcblineut,"g1cblineut")
pop=smbinning.factor.gen(pop,smbpmt,"g1pmt")
pop=smbinning.gen(pop,smbtob,"g1tob")
pop=smbinning.factor.gen(pop,smbdpd,"g1dpd")
pop=smbinning.gen(pop,smbdep,"g1dep")
pop=smbinning.factor.gen(pop,smbod,"g1od")
pop=smbinning.factor.gen(pop,smbhome,"g1home")
pop=smbinning.factor.gen(pop,smbinc,"g1inc")

# Resample
train=subset(pop,rnd<=0.7) # Training sample
test=subset(pop,rnd>0.7) # Testing sample

# Run logistic regression
f=fgood~g1cbs1+g1cbinq+g1cblineut+g1pmt+g1tob+g1dpd+g1dep+g1od+g1home+g1inc
modlogisticsmb=glm(f,data = train,family = binomial())
summary(modlogisticsmb)

# Example: Scaling from logistic parameters to points
smbscaled=smbinning.scaling(modlogisticsmb,pdo=20,score=720,odds=99)
smbscaled$logitscaled # Scaled model
smbscaled$minmaxscore # Expected minimum and maximum Score
smbscaled$parameters # Parameters used for scaling
summary(smbscaled$logitraw) # Extract of original logistic regression

# Example: Generate score from scaled model
pop1=smbinning.scoring.gen(smbscaled=smbscaled, dataset=pop)

# Example Generate SQL code from scaled model
smbinning.scoring.sql(smbscaled)