Homework 3
Hannah Peterson
October 16, 2017
Loan Data
library(car)
set.seed(1)
download.file("https://www.biz.uiowa.edu/faculty/jledolter/datamining/LoanData.csv","LoanData.csv")
LD <- read.csv("LoanData.csv")LD$Status = recode(LD$Status, "'Current'=1; else=0" )
LD$Status = as.numeric(levels(LD$Status)[LD$Status])
n=length(LD$Status)
n## [1] 5611n1=floor(n*(0.6))
n1## [1] 3366n2=n-n1
n2## [1] 2245train=sample(1:n,n1)
XLD <- model.matrix(Status~.,data=LD)[,-1]
XLD[1:3,]## Credit.GradeAA Credit.GradeB Credit.GradeC Credit.GradeD Credit.GradeE
## 1 0 0 1 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## Credit.GradeHR Credit.GradeNC Amount Age Borrower.Rate
## 1 0 0 5000 4 0.150
## 2 1 0 1900 6 0.265
## 3 1 0 1000 3 0.150
## Debt.To.Income.Ratio
## 1 0.04
## 2 0.02
## 3 0.02We know status is the dependent variable because all the other variables determine status. Current status means good loan status and bad or late are bad loan status’s. Recoding them will group the bad loan status’s together.
xtrain <- XLD[train, ]
xnew <- XLD[-train,]
ytrain <- LD$Status[train]
ynew <- LD$Status[-train]
m1=glm(Status~.,family=binomial,data=data.frame(Status=ytrain,xtrain))## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(m1)##
## Call:
## glm(formula = Status ~ ., family = binomial, data = data.frame(Status = ytrain,
## xtrain))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.2961 0.1453 0.2465 0.3981 2.0036
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 7.240e+00 5.089e-01 14.225 < 2e-16 ***
## Credit.GradeAA 9.247e-01 6.850e-01 1.350 0.1770
## Credit.GradeB -2.536e-02 4.489e-01 -0.056 0.9550
## Credit.GradeC 5.488e-01 4.575e-01 1.200 0.2303
## Credit.GradeD 4.003e-01 4.548e-01 0.880 0.3788
## Credit.GradeE 6.680e-03 4.640e-01 0.014 0.9885
## Credit.GradeHR -6.536e-01 4.739e-01 -1.379 0.1679
## Credit.GradeNC -7.227e-01 6.522e-01 -1.108 0.2678
## Amount -3.427e-05 2.072e-05 -1.654 0.0981 .
## Age -3.481e-01 2.815e-02 -12.365 < 2e-16 ***
## Borrower.Rate -1.199e+01 1.636e+00 -7.331 2.28e-13 ***
## Debt.To.Income.Ratio 1.724e-03 3.958e-02 0.044 0.9653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1806.1 on 3365 degrees of freedom
## Residual deviance: 1439.8 on 3354 degrees of freedom
## AIC: 1463.8
##
## Number of Fisher Scoring iterations: 14First 10 predictions
ptest <- predict(m1, newdata = data.frame(xnew), type = "response")
data.frame(ynew,ptest)[1:10,]## ynew ptest
## 1 1 0.9881787
## 6 1 0.9340965
## 12 1 0.7077864
## 13 1 0.9846826
## 15 1 0.8992271
## 16 1 0.8699288
## 17 0 0.7148193
## 18 1 0.7831417
## 20 1 0.8274410
## 21 1 0.9954536bb=cbind(ptest,ynew)
bb1=bb[order(ptest,decreasing=TRUE),]
xbar=mean(ynew)
xbar## [1] 0.9242762axis=dim(n2)
ax=dim(n2)
ay=dim(n2)
axis[1]=1
ax[1]=xbar
ay[1]=bb1[1,2]
for (i in 2:n2) {
axis[i]=i
ax[i]=xbar*i
ay[i]=ay[i-1]+bb1[i,2]
}
aaa=cbind(bb1[,1],bb1[,2],ay,ax)plot(axis,ay,xlab="number of cases",ylab="number of successes",
main="Lift: Cum successes sorted by pred val/success prob")
points(axis,ax,type="l")
Titanic
titanic <- read.csv("/Users/hannahpeterson/Titanic.csv")
## delete last 3 columns
titanic = titanic[-9:-11]
Survived = titanic$Survived
## make dummy variables
a1 = rep(1,length(titanic$Survived))
a2 = rep(0,length(titanic$Survived))
titanic$GenderMale = ifelse(titanic$Gender =="male",a1,a2)
titanic =titanic[-3]
titanic$EmbarkedAtC = ifelse(titanic$EmbarkedAt == "C",a1,a2)
titanic$EmbarkedAtQ = ifelse(titanic$EmbarkedAt == "Q", a1,a2)
titanic = titanic[-7]
NewTitanic = cbind(Survived, Class=titanic$Class, Age=titanic$Age,NumbSibOrSpsAbd =titanic$NumbSibOrSpsAbd, NumbParOrChildAbd =titanic$NumbParOrChildAbd,Fare = titanic$Fare, GenderMale=titanic$GenderMale, EmbarkedAtC = titanic$EmbarkedAtC, EmbarkedAtQ = titanic$EmbarkedAtQ)
n = length(titanic$Survived)
n1=floor(n*(0.6))
n2 = n - n1
train = sample(1:n, n1)
m2 = glm(Survived~., family = binomial, data.frame(NewTitanic))
summary(m2)##
## Call:
## glm(formula = Survived ~ ., family = binomial, data = data.frame(NewTitanic))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5726 -0.6846 -0.4058 0.6456 2.5376
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.534836 0.486022 9.331 < 2e-16 ***
## Class -1.009327 0.133464 -7.563 3.95e-14 ***
## Age -0.037687 0.006634 -5.681 1.34e-08 ***
## NumbSibOrSpsAbd -0.348021 0.108435 -3.209 0.00133 **
## NumbParOrChildAbd 0.049846 0.104193 0.478 0.63236
## Fare 0.000463 0.001934 0.239 0.81084
## GenderMale -2.608938 0.179304 -14.550 < 2e-16 ***
## EmbarkedAtC 0.679066 0.211566 3.210 0.00133 **
## EmbarkedAtQ -0.767411 0.406290 -1.889 0.05892 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1409.99 on 1042 degrees of freedom
## Residual deviance: 954.75 on 1034 degrees of freedom
## AIC: 972.75
##
## Number of Fisher Scoring iterations: 5NewTitanic = NewTitanic[,-1]
Xtrain <- NewTitanic[train, ]
xnew <- NewTitanic[-train,]
ytrain <- Survived[train]
ynew <- Survived[-train]ptest = predict(m2, newdata=data.frame(xnew), type="response")
hist(ptest)
g=floor(ptest+0.4) ## prob .4 or higher
t=table(ynew,g)
t## g
## ynew 0 1
## 0 214 23
## 1 67 114error=(t[1,2]+t[2,1])/n2
## % of Error
error## [1] 0.215311b=cbind(ptest,ynew)
b[1:10,]## ptest ynew
## 1 0.6690433 1
## 2 0.3651202 0
## 3 0.9265782 1
## 4 0.9063001 1
## 5 0.9221009 1
## 6 0.7944524 1
## 7 0.6524415 1
## 8 0.9020214 1
## 9 0.5767017 1
## 10 0.8782562 1b1=b[order(ptest,decreasing=TRUE),]
b1[1:10,]## ptest ynew
## 28 0.9706331 1
## 21 0.9685112 1
## 37 0.9657294 1
## 51 0.9578416 1
## 90 0.9578213 1
## 77 0.9564087 1
## 65 0.9544449 1
## 54 0.9521481 1
## 57 0.9508543 1
## 165 0.9492367 1xbar=mean(ynew)
axis=dim(n2)
ax=dim(n2)
ay=dim(n2)
axis[1]=1
ax[1]=xbar
ay[1]=bb1[1,2]
for (i in 2:n2) {
axis[i]=i
ax[i]=xbar*i
ay[i]=ay[i-1]+bb1[i,2]
}plot(axis,ay,xlab="number of cases",ylab="number of successes",main="Lift: Cum successes sorted by pred val/success prob")
points(axis,ax,type="l")