Added dataset and available code
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dataset/SouthGermanCredit.asc
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dataset/SouthGermanCredit.asc
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dataset/codetable.txt
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dataset/codetable.txt
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$`laufkont = status`
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1 : no checking account
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2 : ... < 0 DM
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3 : 0<= ... < 200 DM
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4 : ... >= 200 DM / salary for at least 1 year
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$`laufzeit = duration`
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$`moral = credit_history`
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0 : delay in paying off in the past
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1 : critical account/other credits elsewhere
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2 : no credits taken/all credits paid back duly
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3 : existing credits paid back duly till now
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4 : all credits at this bank paid back duly
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$`verw = purpose`
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0 : others
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1 : car (new)
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2 : car (used)
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3 : furniture/equipment
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4 : radio/television
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5 : domestic appliances
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6 : repairs
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7 : education
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8 : vacation
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9 : retraining
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10 : business
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$`hoehe = amount`
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$`sparkont = savings`
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1 : unknown/no savings account
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2 : ... < 100 DM
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3 : 100 <= ... < 500 DM
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4 : 500 <= ... < 1000 DM
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5 : ... >= 1000 DM
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$`beszeit = employment_duration`
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1 : unemployed
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2 : < 1 yr
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3 : 1 <= ... < 4 yrs
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4 : 4 <= ... < 7 yrs
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5 : >= 7 yrs
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$`rate = installment_rate`
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1 : >= 35
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2 : 25 <= ... < 35
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3 : 20 <= ... < 25
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4 : < 20
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$`famges = personal_status_sex`
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1 : male : divorced/separated
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2 : female : non-single or male : single
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3 : male : married/widowed
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4 : female : single
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$`buerge = other_debtors`
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1 : none
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2 : co-applicant
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3 : guarantor
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$`wohnzeit = present_residence`
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1 : < 1 yr
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2 : 1 <= ... < 4 yrs
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3 : 4 <= ... < 7 yrs
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4 : >= 7 yrs
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$`verm = property`
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1 : unknown / no property
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2 : car or other
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3 : building soc. savings agr./life insurance
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4 : real estate
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$`alter = age`
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$`weitkred = other_installment_plans`
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1 : bank
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2 : stores
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3 : none
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$`wohn = housing`
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1 : for free
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2 : rent
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3 : own
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$`bishkred = number_credits`
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1 : 1
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2 : 2-3
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3 : 4-5
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4 : >= 6
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$`beruf = job`
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1 : unemployed/unskilled - non-resident
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2 : unskilled - resident
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3 : skilled employee/official
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4 : manager/self-empl./highly qualif. employee
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$`pers = people_liable`
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1 : 3 or more
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2 : 0 to 2
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$`telef = telephone`
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1 : no
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2 : yes (under customer name)
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$`gastarb = foreign_worker`
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1 : yes
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2 : no
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$`kredit = credit_risk`
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0 : bad
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1 : good
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project/read_SouthGermanCredit.R
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project/read_SouthGermanCredit.R
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setwd("/config/workspace/assistenz-r/dataset")
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dat <- read.table("SouthGermanCredit.asc", header=TRUE)
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## dat contains numbers for all variables.
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## variables duration, amount and age are truly quantitative
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## variables installment_rate, present_residence and number_credits are
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### quantitative in the data, but are in fact discretized scores for
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### an underlying quantitative variable
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### and are thus stored as ordered factors below
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## variable people_liable is quantitative in the data but is in fact
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### a binarized score (less 0 to 2 versus 3 or more)
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### and is thus stored as a factor below
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## all the numeric values (=level codes)
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### for the categorical variables
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### (including the discretized quantitative variables),
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### are the P2 scores from Häußler (1979)
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### which can be directly used in credit scoring (larger=better).
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### (Exceptions have been corrected in the raw data,
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### which implies that columns pers and gastarb have
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### entries opposite to those in Open Data LMU (2010)
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### and the GermanCredit data from the UCI ML Repo.)
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## variable names from Fahrmeir/Hamerle book
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nam_fahrmeirbook <- colnames(dat)
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### assign levels
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### level assignment can be sanity-checked
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### with Table 2.1 from the Fahrmeir/Hamerle book,
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### which gives proportions separated for good and bad credit risks.
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### That table is provided with by Open Data LMU
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### (https://doi.org/10.5282/ubm/data.23)
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### together with a German language version of the data set.
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### A corresponding table for the English language data is produced
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### below for the final data (levels ordered by increasing code).
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### Level labels have been taken from package evtree, except for
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### the variable telephone (where the yes level has been made more detailed)
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### and those variables that were quantitative and do not have level labels
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### in evtree.
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nam_evtree <- c("status", "duration", "credit_history", "purpose", "amount",
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"savings", "employment_duration", "installment_rate",
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"personal_status_sex", "other_debtors",
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"present_residence", "property",
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"age", "other_installment_plans",
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"housing", "number_credits",
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"job", "people_liable", "telephone", "foreign_worker",
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"credit_risk")
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names(dat) <- nam_evtree
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## make factors for all except the numeric variables
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## make sure that even empty level of factor purpose = verw (dat[[4]]) is included
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for (i in setdiff(1:21, c(2,4,5,13)))
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dat[[i]] <- factor(dat[[i]])
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## factor purpose
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dat[[4]] <- factor(dat[[4]], levels=as.character(0:10))
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## assign level codes
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## make intrinsically ordered factors into class ordered
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levels(dat$credit_risk) <- c("bad", "good")
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levels(dat$status) = c("no checking account",
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"... < 0 DM",
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"0<= ... < 200 DM",
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"... >= 200 DM / salary for at least 1 year")
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## "critical account/other credits elsewhere" was
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## "critical account/other credits existing (not at this bank)",
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levels(dat$credit_history) <- c(
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"delay in paying off in the past",
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"critical account/other credits elsewhere",
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"no credits taken/all credits paid back duly",
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"existing credits paid back duly till now",
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"all credits at this bank paid back duly")
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levels(dat$purpose) <- c(
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"others",
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"car (new)",
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"car (used)",
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"furniture/equipment",
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"radio/television",
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"domestic appliances",
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"repairs",
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"education",
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"vacation",
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"retraining",
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"business")
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levels(dat$savings) <- c("unknown/no savings account",
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"... < 100 DM",
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"100 <= ... < 500 DM",
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"500 <= ... < 1000 DM",
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"... >= 1000 DM")
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levels(dat$employment_duration) <-
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c( "unemployed",
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"< 1 yr",
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"1 <= ... < 4 yrs",
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"4 <= ... < 7 yrs",
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">= 7 yrs")
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dat$installment_rate <- ordered(dat$installment_rate)
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levels(dat$installment_rate) <- c(">= 35",
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"25 <= ... < 35",
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"20 <= ... < 25",
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"< 20")
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levels(dat$other_debtors) <- c(
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"none",
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"co-applicant",
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"guarantor"
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)
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## female : nonsingle was female : divorced/separated/married
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## widowed females are not mentioned in the code table
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levels(dat$personal_status_sex) <- c(
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"male : divorced/separated",
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"female : non-single or male : single",
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"male : married/widowed",
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"female : single")
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dat$present_residence <- ordered(dat$present_residence)
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levels(dat$present_residence) <- c("< 1 yr",
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"1 <= ... < 4 yrs",
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"4 <= ... < 7 yrs",
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">= 7 yrs")
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## "building soc. savings agr./life insurance",
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## was "building society savings agreement/life insurance"
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levels(dat$property) <- c(
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"unknown / no property",
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"car or other",
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"building soc. savings agr./life insurance",
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"real estate"
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)
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levels(dat$other_installment_plans) <- c(
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"bank",
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"stores",
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"none"
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)
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levels(dat$housing) <- c("for free", "rent", "own")
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dat$number_credits <- ordered(dat$number_credits)
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levels(dat$number_credits) <- c("1", "2-3", "4-5", ">= 6")
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## manager/self-empl./highly qualif. employee was
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## management/self-employed/highly qualified employee/officer
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levels(dat$job) <- c(
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"unemployed/unskilled - non-resident",
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"unskilled - resident",
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"skilled employee/official",
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"manager/self-empl./highly qualif. employee"
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)
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levels(dat$people_liable) <- c("3 or more", "0 to 2")
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levels(dat$telephone) <- c("no", "yes (under customer name)")
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levels(dat$foreign_worker) <- c("yes", "no")
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## checks against fahrmeir table
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tabs <-
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list(status = round(100*prop.table(xtabs(~status+credit_risk, dat),2),2),
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credit_history = round(100*prop.table(xtabs(~credit_history+credit_risk, dat),2),2),
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purpose = round(100*prop.table(xtabs(~purpose+credit_risk, dat),2),2),
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savings = round(100*prop.table(xtabs(~savings+credit_risk, dat),2),2),
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employment_duration = round(100*prop.table(xtabs(~employment_duration+credit_risk, dat),2),2),
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installment_rate = round(100*prop.table(xtabs(~installment_rate+credit_risk, dat),2),2),
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personal_status_sex = round(100*prop.table(xtabs(~personal_status_sex+credit_risk, dat),2),2),
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other_debtors = round(100*prop.table(xtabs(~other_debtors+credit_risk, dat),2),2),
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present_residence = round(100*prop.table(xtabs(~present_residence+credit_risk, dat),2),2),
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property = round(100*prop.table(xtabs(~property+credit_risk, dat),2),2),
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other_installment_plans = round(100*prop.table(xtabs(~other_installment_plans+credit_risk, dat),2),2),
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housing = round(100*prop.table(xtabs(~housing+credit_risk, dat),2),2),
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number_credits = round(100*prop.table(xtabs(~number_credits+credit_risk, dat),2),2),
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job = round(100*prop.table(xtabs(~job+credit_risk, dat),2),2),
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people_liable = round(100*prop.table(xtabs(~people_liable+credit_risk, dat),2),2),
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telephone = round(100*prop.table(xtabs(~telephone+credit_risk, dat),2),2),
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foreign_worker = round(100*prop.table(xtabs(~foreign_worker+credit_risk, dat),2),2))
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## variables for which a tab entry is available
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## (all except 2, 5 and 13)
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tabwhich <- setdiff(1:20, c(2,5,13))
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print(tabs)
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