ECON 122
Make sure you completed the steps in this test assignment’s GitHub
README file. Keep the output type for this .Rmd a github_document.
Use full sentences to answer the questions below. Provide your answer and, if needed, an R chunk used to answer the question. You should be able to complete questions 1-4 with a little review of intro stats R commands. Give questions 5 and 6 a try even if you can’t fully answer them.
The data for today’s review example is a “classic” German credit data
set. Each entry in the data set represents a loan given by the bank,
along personal characteristics of the person the loan was given to, the
type of loan and whether or not they paid it off or defaulted. At the
time, the deutsch mark was the unit of currency so an “DM” in the
dataset refers to that unit of measurement. One of the main questions
about this dataset is to understand factors associated with a loan
defaulting (or not). For this analysis the variable Good.Loan
indicates whether a loan was paid off (GoodLoan) or whether it
defaulted
(BadLoan).
> loans <- read.csv("http://raw.githubusercontent.com/mgelman/data/master/day1CreditData.csv")
> str(loans)
'data.frame': 700 obs. of 21 variables:
$ Status.of.existing.checking.account : Factor w/ 4 levels "... < 0 DM","... >= 200 DM / salary assignments for at least 1 year",..: 1 1 4 4 1 3 1 3 4 1 ...
$ Duration.in.month : int 24 36 48 36 48 48 24 30 36 24 ...
$ Credit.history : Factor w/ 5 levels "all credits at this bank paid back duly",..: 4 2 2 4 5 3 4 4 4 4 ...
$ Purpose : Factor w/ 10 levels "business","car (new)",..: 3 6 8 8 3 5 6 8 2 2 ...
$ Credit.amount : int 6579 2348 3578 2394 4605 6224 4169 1715 909 4817 ...
$ Savings.account.bonds : Factor w/ 5 levels ".. >= 1000 DM",..: 2 2 5 5 2 2 2 5 4 2 ...
$ Present.employment.since : Factor w/ 5 levels ".. >= 7 years",..: 5 3 1 3 1 1 3 3 1 4 ...
$ Installment.rate.in.percentage.of.disposable.income : int 4 3 4 4 3 4 4 4 4 2 ...
$ Personal.status.and.sex : Factor w/ 4 levels "female : divorced/separated/married",..: 4 3 4 1 4 4 4 1 4 4 ...
$ Other.debtors.guarantors : Factor w/ 3 levels "co-applicant",..: 3 3 3 3 3 3 3 3 3 1 ...
$ Present.residence.since : int 2 2 1 4 4 4 4 1 4 3 ...
$ Property : Factor w/ 4 levels "if not A121 : building society savings agreement/life insurance",..: 4 1 3 2 4 4 1 2 1 1 ...
$ Age.in.years : int 29 46 47 25 24 50 28 26 36 31 ...
$ Other.installment.plans : Factor w/ 3 levels "bank","none",..: 2 2 2 2 2 2 2 2 2 2 ...
$ Housing : Factor w/ 3 levels "for free","own",..: 1 2 2 2 1 1 2 2 2 2 ...
$ Number.of.existing.credits.at.this.bank : int 1 2 1 1 2 1 1 1 1 1 ...
$ Job : Factor w/ 4 levels "management/ self-employed/highly qualified employee/ officer",..: 1 2 2 2 2 2 2 2 2 2 ...
$ Number.of.people.being.liable.to.provide.maintenance.for: int 1 1 1 1 2 1 1 1 1 1 ...
$ Telephone : Factor w/ 2 levels "none","yes, registered under the customers name": 2 2 2 1 1 1 1 1 1 2 ...
$ foreign.worker : Factor w/ 2 levels "no","yes": 2 2 2 2 2 2 2 2 2 2 ...
$ Good.Loan : Factor w/ 2 levels "BadLoan","GoodLoan": 2 2 2 2 1 1 2 2 2 1 ...How many loan cases are in the data? How many variables?
answer: There are 700 cases in the data set and 21 variables.
> # R chunk for questions above (if needed)
> dim(loans)
[1] 700 21We will not use all the variables in this handout. Here is a brief description of the variables used in this handout:
| Variable | description |
|---|---|
| Duration.in.month | loan length in months |
| Credit.amount | amount of loan (in DM) |
| Good.Loan | did the loan default or not |
What percentage of loans in this dataset defaulted?
> table(loans$Good.Loan)
BadLoan GoodLoan
218 482
> props <- prop.table(table(loans$Good.Loan))
> props
BadLoan GoodLoan
0.3114286 0.6885714 answer: About 31.1% of these loans defaulted.
What is the average loan duration in this data? Of the loans that defaulted, What percent have a duration of 2 years or less? More than 2 years?
> summary(loans$Duration.in.month)
Min. 1st Qu. Median Mean 3rd Qu. Max.
4.00 12.00 18.00 20.79 24.00 60.00
> default <- loans$Good.Loan == "BadLoan"
> mean(default)
[1] 0.3114286
> # of defaults, what % <= 24?
> perc.24less<- mean(loans$Duration.in.month[default] <=24)
> perc.24less
[1] 0.6880734
> 1-perc.24less
[1] 0.3119266answer: The average loan duration is 20.8 months. Of the loans that defaulted, 68.8% have a duration of 2 years or less. 31.2% have a duration of more than two years
What is the median credit amount for loans that defaulted? for loans that didn’t default? Create a graph that that shows the credit amount distribution for each type of loan (good vs. bad), then compare these distributions.
> summary(loans$Credit.amount)
Min. 1st Qu. Median Mean 3rd Qu. Max.
250 1350 2308 3331 3998 18424
> meds <- tapply(loans$Credit.amount, loans$Good.Loan, median)
> meds
BadLoan GoodLoan
2450.5 2249.0 answer: The median credit amount for loans that defaulted was 2450.5 DM, while the median amount for good loans was 2249 DM.
> boxplot(Credit.amount ~ Good.Loan, data=loans)
Suppose that Barb the data scientist generates the following model criteria to predict when a loan will default. A loan will default if either criteria below is met:
- Duration is longer than 2 years and credit amount is greater than 10,000 DM.
- Duration is 2 years or less and credit amount is less than 2200 DM.
Use this model to predict defaults for the loan data. (Hint: You will
likely need to use the “and” operator &.) Then use the actual and
predicted default variables to find the following rates:
- What is the model’s accuracy rate, i.e. the percentage of all loans that are correctly classified as good or bad?
- What is the model’s false positive rate, i.e. the percentage of good loans that are predicted to default (a “positive” result)? (This is 1 minus the specificity of the model)
- What is the model’s false negative rate, i.e. the percentage of bad loans that are predicted to not default (a “negative” result)? (This is 1 minus the sensitivity of the model)
> pred.Default1 <- ifelse(loans$Duration.in.month > 24 & loans$Credit.amount > 10000,"predBad","predGood")
> table(pred.Default1)
pred.Default1
predBad predGood
26 674
> pred.Default <- ifelse(loans$Duration.in.month <= 24 & loans$Credit.amount < 2200,"predBad",pred.Default1)
> table(pred.Default1,pred.Default)
pred.Default
pred.Default1 predBad predGood
predBad 26 0
predGood 325 349
> table(loans$Good.Loan,pred.Default)
pred.Default
predBad predGood
BadLoan 103 115
GoodLoan 248 234
> props.all<- prop.table(table(loans$Good.Loan,pred.Default))
> props.all
pred.Default
predBad predGood
BadLoan 0.1471429 0.1642857
GoodLoan 0.3542857 0.3342857
> props.byrow<- prop.table(table(loans$Good.Loan,pred.Default),1)
> props.byrow
pred.Default
predBad predGood
BadLoan 0.4724771 0.5275229
GoodLoan 0.5145228 0.4854772answer: For this data, the accuracy rate for this model is 48.1%. The false positive rate is 51.5% and the false negative rate is 52.8%.
Try changing one or two parts of Barb’s simple default criteria and see if you can get a better rates than Barb (higher accuracy and/or lower false rates).