| Title: | Binning Variables to Use in Logistic Regression |
|---|---|
| Description: | Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) <doi:10.1198/106186006X133933>. |
| Authors: | Sneha Tody |
| Maintainer: | Sneha Tody <[email protected]> |
| License: | GPL-2 |
| Version: | 0.3 |
| Built: | 2025-03-15 03:56:41 UTC |
| Source: | https://github.com/cran/logiBin |
This function uses parallel processing to replicate the bins on test data. This can be used to check the stability of the variable.
binTest(binObj, testDf, y, xVars, nCores = 1)binTest(binObj, testDf, y, xVars, nCores = 1)
binObj |
- An object returned by getBins or any other function (except createBins) in this package |
testDf |
- A data frame containing the test data |
y |
- The name of the dependent variable |
xVars |
- A vector names of variables which are to be tested |
nCores |
- The number of cores used for parallel processing. The default value is 1 |
Returns a list containing 2 elements. The first is a data frame called varSummary which contains a summary of the performance of the variables on the test data including their IV value, entropy, flag which indicates if bad rate increases/decreases with variable value, flag to indicate if a monotonic trend is present, number of bins which flip (i.e. do not follow a monotonic trend), number of bins of the variable and a flag to indicate whether it includes pure nodes (node which do not have any defaults). The second element is a data frame called bin which contains details of all the bins of the variables.
b1 <- getBins(loanData, "bad_flag", c('LTV', 'balance')) b2 <- binTest(b1, loanData[1:50,], "bad_flag", c('LTV', 'balance'))b1 <- getBins(loanData, "bad_flag", c('LTV', 'balance')) b2 <- binTest(b1, loanData[1:50,], "bad_flag", c('LTV', 'balance'))
This function creates a data frame with binned variables
createBins(binObj, df, xVars, prefix = "b_")createBins(binObj, df, xVars, prefix = "b_")
binObj |
- An object returned by getBins or any other function in this package |
df |
- A data frame |
xVars |
- A vector of names of variables for which bins have to be created |
prefix |
- The prefix to be added to the variable name to create the new variable. Default value is b_ |
Returns a dataframe which adds the binned variables to the original data frame
b1 <- getBins(loanData, "bad_flag", c('age', 'score', 'balance'), minCr=0.8) loanData <- createBins(b1, loanData, c('age', 'balance'))b1 <- getBins(loanData, "bad_flag", c('age', 'score', 'balance'), minCr=0.8) loanData <- createBins(b1, loanData, c('age', 'balance'))
This function forces a variable to follow a monotonically decreasing trend by grouping bins. In case such a trend can not be forced a message is printed to the console
forceDecrTrend(binObj, xVars)forceDecrTrend(binObj, xVars)
binObj |
- An object returned by getBins or any other function (except createBins) in this package |
xVars |
- A vector of the name of variables |
Returns a list containing 3 objects. Similar to the getBins function
b1 <- getBins(loanData, "bad_flag", c('age', 'score'), minCr=0.6, minProp = 0.01) b1 <- forceDecrTrend(b1, c('score','age'))b1 <- getBins(loanData, "bad_flag", c('age', 'score'), minCr=0.6, minProp = 0.01) b1 <- forceDecrTrend(b1, c('score','age'))
This function forces a variable to follow a monotonically increasing trend by grouping bins. In case such a trend can not be forced a message is printed to the console
forceIncrTrend(binObj, xVars)forceIncrTrend(binObj, xVars)
binObj |
- An object returned by getBins or any other function (except createBins) in this package |
xVars |
- A vector of the name of variables |
Returns a list containing 3 objects. Similar to the getBins function
b1 <- getBins(loanData, "bad_flag", c('age', 'score'), minCr=0.6, minProp = 0.01) b1 <- forceIncrTrend(b1, c('score','age'))b1 <- getBins(loanData, "bad_flag", c('age', 'score'), minCr=0.6, minProp = 0.01) b1 <- forceIncrTrend(b1, c('score','age'))
This function uses parallel processing to compute bins for continuous and categorical variables. The splits are computed using the partykit package which uses conditional inferencing trees. Refer to the package documentation for more details. A separate bin is created for NA values. This can be combined using naCombine function. Categorical variables with a maximum of 10 distinct values are supported.
getBins(df, y, xVars, minProp = 0.03, minCr = 0.9, nCores = 1)getBins(df, y, xVars, minProp = 0.03, minCr = 0.9, nCores = 1)
df |
- A data frame |
y |
- The name of the dependent variable |
xVars |
- A vector names of variables |
minProp |
- The minimum proportion of observations that must be exceeded in order to implement a split. Default value is 0.03 |
minCr |
- The value of test statistic that must be exceeded in order to implement a split. Increasing this value will decrease the number of splits. Refer to the partykit package documentation for more details. Default value is 0.9 |
nCores |
- The number of cores used for parallel processing. The default value is 1 |
Returns a list containing 3 elements. The first is a data frame called varSummary which contains a summary of all the variables along with their IV value, entropy, p value from ctree function in partykit package, flag which indicates if bad rate increases/decreases with variable value, flag to indicate if a monotonic trend is present, number of bins which flip (i.e. do not follow a monotonic trend), number of bins of the variable and a flag to indicate whether it includes pure nodes (node which do not have any defaults). The second element is a data frame called bin which contains details of all the bins of the variables. The third element is a dataframe called err which contains details of all the variables that could not be split and the reason for the same.
b1 <- getBins(loanData, "bad_flag", c('age', 'score', 'balance'))b1 <- getBins(loanData, "bad_flag", c('age', 'score', 'balance'))
A dataset containing simulated data about the characteristic of a customer applying for a loan. The dependent variable is "bad_flag" which indicates whether the customer defaults or not.
loanDataloanData
A data frame with 100 rows and 6 variables:
Indicates whether a customer has defaulted or not
Age of the customer
Ratio of amount of loan to amount of collateral
Indicates the location of the customer
Account balance
credit score of the customer
This function splits variables based on cuts that have been input manually
manualSplit(binObj, splitVar, y, splits, df)manualSplit(binObj, splitVar, y, splits, df)
binObj |
- An object returned by getBins or any other function (except createBins) in this package |
splitVar |
- The name of the variable that has to be split |
y |
- The dependent variable |
splits |
- The splits for the variable |
df |
- A data frame |
Returns a list containing 3 objects. Similar to the getBins function
b1 <- getBins(loanData, "bad_flag", c('age', 'score', 'balance'), minCr=0.8) b1 <- manualSplit(b1, 'age', 'bad_flag', c(25,40,55), loanData)b1 <- getBins(loanData, "bad_flag", c('age', 'score', 'balance'), minCr=0.8) b1 <- manualSplit(b1, 'age', 'bad_flag', c(25,40,55), loanData)
This function combines the NA bin with either the bin having the closest bad rate or the average bad rate if the count of observations in NA bin is low
naCombine(binObj, xVars, cutoffPropn = 0.01)naCombine(binObj, xVars, cutoffPropn = 0.01)
binObj |
- An object returned by getBins or other functions (except createBins) in this package |
xVars |
- A vector of names of variables for which NA bins have to be combined |
cutoffPropn |
- The minimum proportion of observations that must be present in the NA bin for it to be combined with the bin with closest bad rate. If the proportion s below this, the NA bin will be combined with bin having average bad rate |
Returns a list containing 3 objects. Similar to the getBins function
b1 <- getBins(loanData, "bad_flag", c('age', 'score', 'LTV')) b1 <- naCombine(b1, c('LTV'))b1 <- getBins(loanData, "bad_flag", c('age', 'score', 'LTV')) b1 <- naCombine(b1, c('LTV'))