smbinning.scaling: Scaling
In smbinning: Scoring Modeling and Optimal Binning
Description
Usage
Arguments
Value
Examples
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
1
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
1
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# 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)
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
Call:
glm(formula = f, family = binomial(), data = train)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.4327 0.1139 0.2992 0.5367 1.9285
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.64857 0.46933 3.513 0.000444 ***
g1cbs101 <= 38.81 -0.28377 0.28880 -0.983 0.325813
g1cbs102 <= 51.7701 -0.08315 0.25530 -0.326 0.744656
g1cbs103 <= 65.36 0.23280 0.26466 0.880 0.379068
g1cbs104 > 65.36 1.41459 0.42884 3.299 0.000972 ***
g1cbinq02 = '01' -0.50290 0.24139 -2.083 0.037215 *
g1cbinq03 = '02' -0.52214 0.26268 -1.988 0.046837 *
g1cblineut02 <= 40 0.30958 0.26469 1.170 0.242172
g1cblineut03 <= 50 -0.03859 0.24704 -0.156 0.875880
g1cblineut04 > 50 -0.15797 0.24977 -0.632 0.527088
g1pmt02 = 'M' -0.35568 0.17135 -2.076 0.037918 *
g1pmt03 = 'P' 0.81906 0.35739 2.292 0.021917 *
g1tob02 <= 2 0.04055 0.18687 0.217 0.828225
g1tob03 <= 3 0.77891 0.23268 3.348 0.000815 ***
g1tob04 > 3 0.97801 0.27799 3.518 0.000435 ***
g1dpd02 = '01Lo' -0.44961 0.19375 -2.321 0.020310 *
g1dpd03 = '02Hi' -0.90528 0.20462 -4.424 0.00000968 ***
g1dep02 <= 12000 0.13422 0.18412 0.729 0.466026
g1dep03 <= 15000 0.64105 0.20970 3.057 0.002235 **
g1dep04 > 15000 0.69876 0.32640 2.141 0.032291 *
g1od02 = '01' -0.29232 0.19239 -1.519 0.128658
g1od03 = '02' -0.60999 0.18296 -3.334 0.000856 ***
g1home02 = 'Yes' 0.25855 0.23341 1.108 0.267991
g1inc01 in ('W01','W02') -0.50407 0.30014 -1.679 0.093066 .
g1inc02 in ('W03','W04','W05') -0.42082 0.27068 -1.555 0.120024
g1inc03 in ('W06','W07') 0.03864 0.28624 0.135 0.892607
g1inc04 in ('W08','W09','W10') 0.63195 0.28940 2.184 0.028985 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1807.1 on 1758 degrees of freedom
Residual deviance: 1249.5 on 1732 degrees of freedom
AIC: 1303.5
Number of Fisher Scoring iterations: 6
[[1]]
Characteristic Attribute Coefficient Weight WeightScaled
1 (Intercept) 1.64856917 47.567651 0.00000
2 g1cbs1 00 Miss 0.00000000 0.000000 63.49805
3 g1cbs1 01 <= 38.81 -0.28376561 -8.187745 55.31031
4 g1cbs1 02 <= 51.7701 -0.08315125 -2.399238 61.09881
5 g1cbs1 03 <= 65.36 0.23279610 6.717076 70.21513
6 g1cbs1 04 > 65.36 1.41459065 40.816458 104.31451
7 g1cbinq 01 = '00' 0.00000000 0.000000 63.49805
8 g1cbinq 02 = '01' -0.50290341 -14.510725 48.98733
9 g1cbinq 03 = '02' -0.52214326 -15.065870 48.43218
10 g1cblineut 01 <= 30 0.00000000 0.000000 63.49805
11 g1cblineut 02 <= 40 0.30957673 8.932496 72.43055
12 g1cblineut 03 <= 50 -0.03858686 -1.113381 62.38467
13 g1cblineut 04 > 50 -0.15796860 -4.558010 58.94004
14 g1pmt 01 = 'A' 0.00000000 0.000000 63.49805
15 g1pmt 02 = 'M' -0.35567877 -10.262720 53.23533
16 g1pmt 03 = 'P' 0.81905784 23.633014 87.13107
17 g1tob 01 <= 1 0.00000000 0.000000 63.49805
18 g1tob 02 <= 2 0.04054600 1.169910 64.66796
19 g1tob 03 <= 3 0.77891039 22.474603 85.97266
20 g1tob 04 > 3 0.97800870 28.219366 91.71742
21 g1dpd 01 = '00No' 0.00000000 0.000000 63.49805
22 g1dpd 02 = '01Lo' -0.44960718 -12.972921 50.52513
23 g1dpd 03 = '02Hi' -0.90528183 -26.120912 37.37714
24 g1dep 01 <= 10000 0.00000000 0.000000 63.49805
25 g1dep 02 <= 12000 0.13421578 3.872649 67.37070
26 g1dep 03 <= 15000 0.64104694 18.496705 81.99476
27 g1dep 04 > 15000 0.69875582 20.161831 83.65988
28 g1od 01 = '00' 0.00000000 0.000000 63.49805
29 g1od 02 = '01' -0.29231846 -8.434528 55.06352
30 g1od 03 = '02' -0.60998724 -17.600511 45.89754
31 g1home 01 = 'No' 0.00000000 0.000000 63.49805
32 g1home 02 = 'Yes' 0.25855267 7.460253 70.95830
33 g1inc 00 Miss 0.00000000 0.000000 63.49805
34 g1inc 01 in ('W01','W02') -0.50406837 -14.544339 48.95371
35 g1inc 02 in ('W03','W04','W05') -0.42082027 -12.142306 51.35575
36 g1inc 03 in ('W06','W07') 0.03864433 1.115040 64.61309
37 g1inc 04 in ('W08','W09','W10') 0.63194734 18.234146 81.73220
Points
1 0
2 63
3 55
4 61
5 70
6 104
7 63
8 49
9 48
10 63
11 72
12 62
13 59
14 63
15 53
16 87
17 63
18 65
19 86
20 92
21 63
22 51
23 37
24 63
25 67
26 82
27 84
28 63
29 55
30 46
31 63
32 71
33 63
34 49
35 51
36 65
37 82
[1] 536 781
$pdo
[1] 20
$odds
[1] 99
$factor
[1] 28.8539
$score
[1] 720
$offset
[1] 587.4129
$intercept
[1] 1.648569
$interceptweight
[1] 47.56765
$nchr
[1] 10
$interceptbychr
[1] 4.756765
$offsetbychr
[1] 58.74129
$scorebychr
[1] 63.49805
Call:
glm(formula = f, family = binomial(), data = train)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.4327 0.1139 0.2992 0.5367 1.9285
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.64857 0.46933 3.513 0.000444 ***
g1cbs101 <= 38.81 -0.28377 0.28880 -0.983 0.325813
g1cbs102 <= 51.7701 -0.08315 0.25530 -0.326 0.744656
g1cbs103 <= 65.36 0.23280 0.26466 0.880 0.379068
g1cbs104 > 65.36 1.41459 0.42884 3.299 0.000972 ***
g1cbinq02 = '01' -0.50290 0.24139 -2.083 0.037215 *
g1cbinq03 = '02' -0.52214 0.26268 -1.988 0.046837 *
g1cblineut02 <= 40 0.30958 0.26469 1.170 0.242172
g1cblineut03 <= 50 -0.03859 0.24704 -0.156 0.875880
g1cblineut04 > 50 -0.15797 0.24977 -0.632 0.527088
g1pmt02 = 'M' -0.35568 0.17135 -2.076 0.037918 *
g1pmt03 = 'P' 0.81906 0.35739 2.292 0.021917 *
g1tob02 <= 2 0.04055 0.18687 0.217 0.828225
g1tob03 <= 3 0.77891 0.23268 3.348 0.000815 ***
g1tob04 > 3 0.97801 0.27799 3.518 0.000435 ***
g1dpd02 = '01Lo' -0.44961 0.19375 -2.321 0.020310 *
g1dpd03 = '02Hi' -0.90528 0.20462 -4.424 0.00000968 ***
g1dep02 <= 12000 0.13422 0.18412 0.729 0.466026
g1dep03 <= 15000 0.64105 0.20970 3.057 0.002235 **
g1dep04 > 15000 0.69876 0.32640 2.141 0.032291 *
g1od02 = '01' -0.29232 0.19239 -1.519 0.128658
g1od03 = '02' -0.60999 0.18296 -3.334 0.000856 ***
g1home02 = 'Yes' 0.25855 0.23341 1.108 0.267991
g1inc01 in ('W01','W02') -0.50407 0.30014 -1.679 0.093066 .
g1inc02 in ('W03','W04','W05') -0.42082 0.27068 -1.555 0.120024
g1inc03 in ('W06','W07') 0.03864 0.28624 0.135 0.892607
g1inc04 in ('W08','W09','W10') 0.63195 0.28940 2.184 0.028985 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1807.1 on 1758 degrees of freedom
Residual deviance: 1249.5 on 1732 degrees of freedom
AIC: 1303.5
Number of Fisher Scoring iterations: 6
-- Replace 'TableName' with your table in SQL
alter table TableName add
g1cbs1Points int not null,
g1cbinqPoints int not null,
g1cblineutPoints int not null,
g1pmtPoints int not null,
g1tobPoints int not null,
g1dpdPoints int not null,
g1depPoints int not null,
g1odPoints int not null,
g1homePoints int not null,
g1incPoints int not null,
Score int not null
go
-- Replace 'TableName' with your table in SQL
update TableName set
g1cbs1Points=(
case
when g1cbs1 = '00 Miss' then 63
when g1cbs1 = '01 <= 38.81' then 55
when g1cbs1 = '02 <= 51.7701' then 61
when g1cbs1 = '03 <= 65.36' then 70
when g1cbs1 = '04 > 65.36' then 104
else Null end
),
g1cbinqPoints=(
case
when g1cbinq = '01 = ''00''' then 63
when g1cbinq = '02 = ''01''' then 49
when g1cbinq = '03 = ''02''' then 48
else Null end
),
g1cblineutPoints=(
case
when g1cblineut = '01 <= 30' then 63
when g1cblineut = '02 <= 40' then 72
when g1cblineut = '03 <= 50' then 62
when g1cblineut = '04 > 50' then 59
else Null end
),
g1pmtPoints=(
case
when g1pmt = '01 = ''A''' then 63
when g1pmt = '02 = ''M''' then 53
when g1pmt = '03 = ''P''' then 87
else Null end
),
g1tobPoints=(
case
when g1tob = '01 <= 1' then 63
when g1tob = '02 <= 2' then 65
when g1tob = '03 <= 3' then 86
when g1tob = '04 > 3' then 92
else Null end
),
g1dpdPoints=(
case
when g1dpd = '01 = ''00No''' then 63
when g1dpd = '02 = ''01Lo''' then 51
when g1dpd = '03 = ''02Hi''' then 37
else Null end
),
g1depPoints=(
case
when g1dep = '01 <= 10000' then 63
when g1dep = '02 <= 12000' then 67
when g1dep = '03 <= 15000' then 82
when g1dep = '04 > 15000' then 84
else Null end
),
g1odPoints=(
case
when g1od = '01 = ''00''' then 63
when g1od = '02 = ''01''' then 55
when g1od = '03 = ''02''' then 46
else Null end
),
g1homePoints=(
case
when g1home = '01 = ''No''' then 63
when g1home = '02 = ''Yes''' then 71
else Null end
),
g1incPoints=(
case
when g1inc = '00 Miss' then 63
when g1inc = '01 in (''W01'',''W02'')' then 49
when g1inc = '02 in (''W03'',''W04'',''W05'')' then 51
when g1inc = '03 in (''W06'',''W07'')' then 65
when g1inc = '04 in (''W08'',''W09'',''W10'')' then 82
else Null end
),
Score=(
g1cbs1Points + g1cbinqPoints + g1cblineutPoints + g1pmtPoints + g1tobPoints + g1dpdPoints + g1depPoints + g1odPoints + g1homePoints + g1incPoints
)
smbinning documentation built on May 1, 2019, 10:06 p.m.
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Description Usage Arguments Value Examples
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
1 | smbinning.scaling(logitraw, pdo = 20, score = 720, odds = 99)
|
Arguments
logitraw |
Logistic regression (glm) that must have specified |
pdo |
Points to double the oods. |
score |
Score at which the desire |
odds |
Desired |
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | # 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)
|
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
Call:
glm(formula = f, family = binomial(), data = train)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.4327 0.1139 0.2992 0.5367 1.9285
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.64857 0.46933 3.513 0.000444 ***
g1cbs101 <= 38.81 -0.28377 0.28880 -0.983 0.325813
g1cbs102 <= 51.7701 -0.08315 0.25530 -0.326 0.744656
g1cbs103 <= 65.36 0.23280 0.26466 0.880 0.379068
g1cbs104 > 65.36 1.41459 0.42884 3.299 0.000972 ***
g1cbinq02 = '01' -0.50290 0.24139 -2.083 0.037215 *
g1cbinq03 = '02' -0.52214 0.26268 -1.988 0.046837 *
g1cblineut02 <= 40 0.30958 0.26469 1.170 0.242172
g1cblineut03 <= 50 -0.03859 0.24704 -0.156 0.875880
g1cblineut04 > 50 -0.15797 0.24977 -0.632 0.527088
g1pmt02 = 'M' -0.35568 0.17135 -2.076 0.037918 *
g1pmt03 = 'P' 0.81906 0.35739 2.292 0.021917 *
g1tob02 <= 2 0.04055 0.18687 0.217 0.828225
g1tob03 <= 3 0.77891 0.23268 3.348 0.000815 ***
g1tob04 > 3 0.97801 0.27799 3.518 0.000435 ***
g1dpd02 = '01Lo' -0.44961 0.19375 -2.321 0.020310 *
g1dpd03 = '02Hi' -0.90528 0.20462 -4.424 0.00000968 ***
g1dep02 <= 12000 0.13422 0.18412 0.729 0.466026
g1dep03 <= 15000 0.64105 0.20970 3.057 0.002235 **
g1dep04 > 15000 0.69876 0.32640 2.141 0.032291 *
g1od02 = '01' -0.29232 0.19239 -1.519 0.128658
g1od03 = '02' -0.60999 0.18296 -3.334 0.000856 ***
g1home02 = 'Yes' 0.25855 0.23341 1.108 0.267991
g1inc01 in ('W01','W02') -0.50407 0.30014 -1.679 0.093066 .
g1inc02 in ('W03','W04','W05') -0.42082 0.27068 -1.555 0.120024
g1inc03 in ('W06','W07') 0.03864 0.28624 0.135 0.892607
g1inc04 in ('W08','W09','W10') 0.63195 0.28940 2.184 0.028985 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1807.1 on 1758 degrees of freedom
Residual deviance: 1249.5 on 1732 degrees of freedom
AIC: 1303.5
Number of Fisher Scoring iterations: 6
[[1]]
Characteristic Attribute Coefficient Weight WeightScaled
1 (Intercept) 1.64856917 47.567651 0.00000
2 g1cbs1 00 Miss 0.00000000 0.000000 63.49805
3 g1cbs1 01 <= 38.81 -0.28376561 -8.187745 55.31031
4 g1cbs1 02 <= 51.7701 -0.08315125 -2.399238 61.09881
5 g1cbs1 03 <= 65.36 0.23279610 6.717076 70.21513
6 g1cbs1 04 > 65.36 1.41459065 40.816458 104.31451
7 g1cbinq 01 = '00' 0.00000000 0.000000 63.49805
8 g1cbinq 02 = '01' -0.50290341 -14.510725 48.98733
9 g1cbinq 03 = '02' -0.52214326 -15.065870 48.43218
10 g1cblineut 01 <= 30 0.00000000 0.000000 63.49805
11 g1cblineut 02 <= 40 0.30957673 8.932496 72.43055
12 g1cblineut 03 <= 50 -0.03858686 -1.113381 62.38467
13 g1cblineut 04 > 50 -0.15796860 -4.558010 58.94004
14 g1pmt 01 = 'A' 0.00000000 0.000000 63.49805
15 g1pmt 02 = 'M' -0.35567877 -10.262720 53.23533
16 g1pmt 03 = 'P' 0.81905784 23.633014 87.13107
17 g1tob 01 <= 1 0.00000000 0.000000 63.49805
18 g1tob 02 <= 2 0.04054600 1.169910 64.66796
19 g1tob 03 <= 3 0.77891039 22.474603 85.97266
20 g1tob 04 > 3 0.97800870 28.219366 91.71742
21 g1dpd 01 = '00No' 0.00000000 0.000000 63.49805
22 g1dpd 02 = '01Lo' -0.44960718 -12.972921 50.52513
23 g1dpd 03 = '02Hi' -0.90528183 -26.120912 37.37714
24 g1dep 01 <= 10000 0.00000000 0.000000 63.49805
25 g1dep 02 <= 12000 0.13421578 3.872649 67.37070
26 g1dep 03 <= 15000 0.64104694 18.496705 81.99476
27 g1dep 04 > 15000 0.69875582 20.161831 83.65988
28 g1od 01 = '00' 0.00000000 0.000000 63.49805
29 g1od 02 = '01' -0.29231846 -8.434528 55.06352
30 g1od 03 = '02' -0.60998724 -17.600511 45.89754
31 g1home 01 = 'No' 0.00000000 0.000000 63.49805
32 g1home 02 = 'Yes' 0.25855267 7.460253 70.95830
33 g1inc 00 Miss 0.00000000 0.000000 63.49805
34 g1inc 01 in ('W01','W02') -0.50406837 -14.544339 48.95371
35 g1inc 02 in ('W03','W04','W05') -0.42082027 -12.142306 51.35575
36 g1inc 03 in ('W06','W07') 0.03864433 1.115040 64.61309
37 g1inc 04 in ('W08','W09','W10') 0.63194734 18.234146 81.73220
Points
1 0
2 63
3 55
4 61
5 70
6 104
7 63
8 49
9 48
10 63
11 72
12 62
13 59
14 63
15 53
16 87
17 63
18 65
19 86
20 92
21 63
22 51
23 37
24 63
25 67
26 82
27 84
28 63
29 55
30 46
31 63
32 71
33 63
34 49
35 51
36 65
37 82
[1] 536 781
$pdo
[1] 20
$odds
[1] 99
$factor
[1] 28.8539
$score
[1] 720
$offset
[1] 587.4129
$intercept
[1] 1.648569
$interceptweight
[1] 47.56765
$nchr
[1] 10
$interceptbychr
[1] 4.756765
$offsetbychr
[1] 58.74129
$scorebychr
[1] 63.49805
Call:
glm(formula = f, family = binomial(), data = train)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.4327 0.1139 0.2992 0.5367 1.9285
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.64857 0.46933 3.513 0.000444 ***
g1cbs101 <= 38.81 -0.28377 0.28880 -0.983 0.325813
g1cbs102 <= 51.7701 -0.08315 0.25530 -0.326 0.744656
g1cbs103 <= 65.36 0.23280 0.26466 0.880 0.379068
g1cbs104 > 65.36 1.41459 0.42884 3.299 0.000972 ***
g1cbinq02 = '01' -0.50290 0.24139 -2.083 0.037215 *
g1cbinq03 = '02' -0.52214 0.26268 -1.988 0.046837 *
g1cblineut02 <= 40 0.30958 0.26469 1.170 0.242172
g1cblineut03 <= 50 -0.03859 0.24704 -0.156 0.875880
g1cblineut04 > 50 -0.15797 0.24977 -0.632 0.527088
g1pmt02 = 'M' -0.35568 0.17135 -2.076 0.037918 *
g1pmt03 = 'P' 0.81906 0.35739 2.292 0.021917 *
g1tob02 <= 2 0.04055 0.18687 0.217 0.828225
g1tob03 <= 3 0.77891 0.23268 3.348 0.000815 ***
g1tob04 > 3 0.97801 0.27799 3.518 0.000435 ***
g1dpd02 = '01Lo' -0.44961 0.19375 -2.321 0.020310 *
g1dpd03 = '02Hi' -0.90528 0.20462 -4.424 0.00000968 ***
g1dep02 <= 12000 0.13422 0.18412 0.729 0.466026
g1dep03 <= 15000 0.64105 0.20970 3.057 0.002235 **
g1dep04 > 15000 0.69876 0.32640 2.141 0.032291 *
g1od02 = '01' -0.29232 0.19239 -1.519 0.128658
g1od03 = '02' -0.60999 0.18296 -3.334 0.000856 ***
g1home02 = 'Yes' 0.25855 0.23341 1.108 0.267991
g1inc01 in ('W01','W02') -0.50407 0.30014 -1.679 0.093066 .
g1inc02 in ('W03','W04','W05') -0.42082 0.27068 -1.555 0.120024
g1inc03 in ('W06','W07') 0.03864 0.28624 0.135 0.892607
g1inc04 in ('W08','W09','W10') 0.63195 0.28940 2.184 0.028985 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1807.1 on 1758 degrees of freedom
Residual deviance: 1249.5 on 1732 degrees of freedom
AIC: 1303.5
Number of Fisher Scoring iterations: 6
-- Replace 'TableName' with your table in SQL
alter table TableName add
g1cbs1Points int not null,
g1cbinqPoints int not null,
g1cblineutPoints int not null,
g1pmtPoints int not null,
g1tobPoints int not null,
g1dpdPoints int not null,
g1depPoints int not null,
g1odPoints int not null,
g1homePoints int not null,
g1incPoints int not null,
Score int not null
go
-- Replace 'TableName' with your table in SQL
update TableName set
g1cbs1Points=(
case
when g1cbs1 = '00 Miss' then 63
when g1cbs1 = '01 <= 38.81' then 55
when g1cbs1 = '02 <= 51.7701' then 61
when g1cbs1 = '03 <= 65.36' then 70
when g1cbs1 = '04 > 65.36' then 104
else Null end
),
g1cbinqPoints=(
case
when g1cbinq = '01 = ''00''' then 63
when g1cbinq = '02 = ''01''' then 49
when g1cbinq = '03 = ''02''' then 48
else Null end
),
g1cblineutPoints=(
case
when g1cblineut = '01 <= 30' then 63
when g1cblineut = '02 <= 40' then 72
when g1cblineut = '03 <= 50' then 62
when g1cblineut = '04 > 50' then 59
else Null end
),
g1pmtPoints=(
case
when g1pmt = '01 = ''A''' then 63
when g1pmt = '02 = ''M''' then 53
when g1pmt = '03 = ''P''' then 87
else Null end
),
g1tobPoints=(
case
when g1tob = '01 <= 1' then 63
when g1tob = '02 <= 2' then 65
when g1tob = '03 <= 3' then 86
when g1tob = '04 > 3' then 92
else Null end
),
g1dpdPoints=(
case
when g1dpd = '01 = ''00No''' then 63
when g1dpd = '02 = ''01Lo''' then 51
when g1dpd = '03 = ''02Hi''' then 37
else Null end
),
g1depPoints=(
case
when g1dep = '01 <= 10000' then 63
when g1dep = '02 <= 12000' then 67
when g1dep = '03 <= 15000' then 82
when g1dep = '04 > 15000' then 84
else Null end
),
g1odPoints=(
case
when g1od = '01 = ''00''' then 63
when g1od = '02 = ''01''' then 55
when g1od = '03 = ''02''' then 46
else Null end
),
g1homePoints=(
case
when g1home = '01 = ''No''' then 63
when g1home = '02 = ''Yes''' then 71
else Null end
),
g1incPoints=(
case
when g1inc = '00 Miss' then 63
when g1inc = '01 in (''W01'',''W02'')' then 49
when g1inc = '02 in (''W03'',''W04'',''W05'')' then 51
when g1inc = '03 in (''W06'',''W07'')' then 65
when g1inc = '04 in (''W08'',''W09'',''W10'')' then 82
else Null end
),
Score=(
g1cbs1Points + g1cbinqPoints + g1cblineutPoints + g1pmtPoints + g1tobPoints + g1dpdPoints + g1depPoints + g1odPoints + g1homePoints + g1incPoints
)
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