# WOETable

### Description

Compute the WOETable that shows the Weights Of Evidence (WOE) for each group and respeective Information Values (IVs).

### Usage

1 | ```
WOETable(X, Y, valueOfGood = 1)
``` |

### Arguments

`X` |
The categorical variable stored as factor for which WOE Table is to be computed. |

`Y` |
The actual 1/0 flags for the binary response variable. It can take values of either 1 or 0, where 1 represents the 'Good' or 'Events' while 0 represents 'Bad' or 'Non-Events'. |

`valueOfGood` |
The value in Y that is used to represent 'Good' or the occurence of the event of interest. Defaults to 1. |

### Details

For a given actual for a Binary Y variable and a categorical X variable stored as factor, the WOE table is generated with calculated WOE's and IV's

### Value

The WOE table with the respective weights of evidence for each group and the IV's.

CAT. The groups (levels) of the categorical X variable for which WOE is to be calculated.

GOODS. The total number of "Goods" or "Events" in respective group.

BADS. The total number of "Bads" or "Non-Events" in respective group.

TOTAL. The total number of observations in respective group.

PCT_G. The Percentage of 'Goods' or 'Events' accounted for by respective group.

PCT_B. The Percentage of 'Bads' or 'Non-Events' accounted for by respective group.

WOE. The computed weights of evidence(WOE) for respective group. The WOE values can be used in place of the actual group itself, thereby producing a 'continuous' alternative.

IV. The information value contributed by each group in the X. The sum of IVs is the total information value of the categorical X variable.

### Author(s)

Selva Prabhakaran selva86@gmail.com

### Examples

1 2 | ```
data('SimData')
WOETable(X=SimData$X.Cat, Y=SimData$Y.Binary)
``` |