ITrees Extended Options - Quick Tab

Select the Quick tab of the ITrees Extended Options dialog box to access options to select the variables and the codes of variables required for the Type of analysis selected on the Startup Panel - Quick tab. The variables and code selection options are described here.

Element Name Description
Variables Click this button to display a four-variable lists selection dialog box from which you can select the dependent variable, categorical predictor variables, continuous predictors, and count variable (optional).

From the first list, select the dependent variable. If you are analyzing a classification problem with a categorical response variable, the dependent variable must contain text or numeric (integer) codes that identify the class or group to which each case or observation belongs.

From the second list, select the categorical variables (factors). Note that these variables must contain text or numeric codes that identify the classes or groups to which each case or observation belongs.

From the third list, select the continuous predictors.

From the fourth list, select the count variable. This variable is optional. If selected, it will be used as a case-multiplier in the analysis. Thus, by using a count variable you can analyze input data that have previously been aggregated (tabulated).

Use dependent as ordinal variable. Ordered Twoing Criterion is used. Select this check box to analyze a dependent (criterion) variable that is ordinal in nature. When this check box is selected, the Response codes button will become active provided you have specified a dependent variable. A brief description of how the ordered twoing criterion is used to split a node is given below.

At each node the following computations are performed.

  1.  Denote the classes of the ordinal response variable as C = {1,2,…J}.
  2.  Create two composite classes C1 and C2 = C-C1 such that C1 is of the form {1,2,…jn}.
  3.  Compute the decrease in the impurity measure as though this were a two class problem using the Gini criterion (for the two class problem this is equivalent to the twoing criterion) for all possible splits.
  4.  Find the classes C1 and C2 such that this decrease in the impurity measure is maximized.
Response codes This button is available only after a dependent variable has been specified, and if the current Type of analysis (as selected on the Interactive Trees Startup Panel - Quick tab) is a Classification Analysis. In this case, the dependent variable is assumed to be categorical in nature, and the object of the analysis is to correctly classify the cases (observations) into groups specified by the dependent variable. Click the Response codes button to display the Select codes for the categorical response dialog box, in which you make a selection for the Categorical response from the list of codes for the Dependent variable selected in the current analysis. The list of codes can be displayed by clicking the Zoom button. Note that you must select at least two codes for the selected Dependent variable.
Factor codes Once you have selected categorical predictor variables, the button for the selection of the factor codes will be available. Click the Factor codes button to display the Select Codes for Independent Variables dialog box, in which you select codes for the Categorical factors. The list of codes can be displayed by clicking the Zoom button. Note that you must select at least two codes for each of the selected Categorical factors. The selected factor codes are treated as case selection conditions, so cases that have an observed factor code that is not included in the selected set will be ignored in the analysis.