| Type: | Package |
| Title: | Computes Weight of Evidence and Information Values |
| Version: | 0.2 |
| Date: | 2015-07-07 |
| Author: | Sudarson Mothilal Thoppay <sudarson@outlook.com> |
| Maintainer: | Sudarson Mothilal Thoppay <sudarson@outlook.com> |
| Description: | Shows the relationship between an independent and dependent variable through Weight of Evidence and Information Value. |
| Depends: | R (≥ 3.1.0) |
| License: | GPL-2 |
| Repository: | CRAN |
| Packaged: | 2015-07-28 17:55:30 UTC; Muruga |
| NeedsCompilation: | no |
| Date/Publication: | 2015-07-28 23:41:30 |
Weigth of Evidence
Description
Computes the Weight of Evidence and Information Value between Dependent and Independent variable.
Usage
woe(Data, Independent, Continuous, Dependent, C_Bin, Bad, Good)
Arguments
Data |
: Name of Data Set |
Independent |
: Name of the Independent Variable |
Continuous |
: True if the variable is continuous, False if variable is Ordinal or Nominal |
Dependent |
: Name of the Targer Variable |
C_Bin |
: Count of Bins to be computed |
Bad |
: Which categorical variable do you want to be bad |
Good |
: Which categorical variable do you want to be good |
Details
WOE
Value
Returns a DataSet with computed WoE and IV values on success or 0 on Failure
Note
"woe" shows the log-odds ratio between between Goods and Bads. In the Bivalued Dependenet variable, one value represents Goods and others are bads. In Detail with an Example: Let Dependent varaible be ATTRITED (0,1) and Independent variable be TENURE where, 1-Attrited, 0-Non Attrited. If I wish to check WOE and IV of Tenure with ATTRITED to know if Tenure has an effect in getting attrited, Then good would be 1 and bad=0. If I wish to check WOE and IV of Tenure with ATTRITED to know if Tenure has an effect in not getting attrited, Then good would be 0 and bad=1.
Author(s)
Sudarson Mothilal Thoppay
Examples
woe(Data=mtcars,"cyl",FALSE,"am",10,Bad=0,Good=1)
woe(Data=mtcars,"mpg",TRUE,"am",10,Bad=0,Good=1)