Identify Unusual Cases Options
Criteria for Identifying Unusual Cases. These selections determine how many cases are included in the anomaly list.
- Percentage of cases with highest anomaly index values. Specify a positive number that is less than or equal to 100.
- Fixed number of cases with highest anomaly index values. Specify a positive integer that is less than or equal to the total number of cases in the active dataset that are used in the analysis.
- Identify only cases whose anomaly index value meets or exceeds a minimum value. Specify a non-negative number. A case is considered anomalous if its anomaly index value is larger than or equal to the specified cutoff point. This option is used together with the Percentage of cases and Fixed number of cases options. For example, if you specify a fixed number of 50 cases and a cutoff value of 2, the anomaly list will consist of, at most, 50 cases, each with an anomaly index value that is larger than or equal to 2.
Number of Peer Groups. The procedure will search for the best number of peer groups between the specified minimum and maximum values. The values must be positive integers, and the minimum must not exceed the maximum. When the specified values are equal, the procedure assumes a fixed number of peer groups.
Note: Depending on the amount of variation in your data, there may be situations in which the number of peer groups that the data can support is less than the number specified as the minimum. In such a situation, the procedure may produce a smaller number of peer groups.
Maximum Number of Reasons. A reason consists of the variable impact measure, the variable name for this reason, the value of the variable, and the value of the corresponding peer group. Specify a non-negative integer; if this value equals or exceeds the number of processed variables that are used in the analysis, all variables are shown.
How to specify model options
- From the menus choose:
- In the Identify Unusual Cases dialog box, click the Options tab.