Manual Discretization

Context

Manual Discretization

  • Select Manual from the drop-down menu.

  • Several additional items and buttons appear on the left side, plus a Cumulative Distribution Function (CDF) is shown on the right. This CDF plot can help in selecting appropriate discretization intervals.

  • In the screenshot below, the variable Standing Height (cm) is selected, meaning that the CDF plot corresponds to that variable.

  • Click on the Density Function button, and the Probability Density Function (PDF) of the same variable appears.

  • Now the button reads Distribution Function, and by clicking it, you can toggle back to the CDF view.

  • By default, only one threshold is placed at the mean value of the corresponding variable.

  • This threshold appears as a horizontal line on the CDF and a vertical line on the PDF.

  • The CDF and PDF plots are interactive; you can add, delete, and modify thresholds.

Editing Thresholds

The following instructions apply to both plots:

  • To select a threshold, left-click on that threshold.

  • The selected threshold is highlighted in red.

  • The remaining thresholds on the plot remain blue.

  • The precise numerical value of a selected threshold is shown in the Threshold Value field to the right of the plot.

  • To move a threshold, click on it and hold, then move it. Release to fix its position.

  • The percentages displayed at the end of a selected threshold refer to the share of observations that fall into the intervals above and below this threshold.

  • Instead of moving the selected threshold with your cursor, you can type a specific value into the Threshold Value field.

  • To add an additional threshold, right-click with your cursor on the desired position.

  • To remove an existing threshold, right-click on it to delete it.

  • A zoom function is available for examining the plot in detail:

    • Hold the Ctrl key, click and hold the left mouse button, then move the cursor across the range you wish to focus.

      • To revert to the default zoom, hold Ctrl, then double-click anywhere in the plot area.

      • You can zoom in repeatedly until you have reached the desired magnification level.

  • As an alternative to selecting a threshold by left-clicking, you can scroll through all thresholds using the Previous and Next buttons.

Note that as soon as a threshold is defined on a Continuous variable, it is considered Discretized, and the variable's data column is colored in soft blue.

The interactive CDF and PDF plots are similar to the editing functions available under Curve View in the Node Editor.

Workflow Illustration

We re-use the dataset from the previous steps, so we can fast-forward to Step 4 and focus on that step.

Generate a Discretization

  • While remaining on the Manual Discretization screen, you can also utilize the Generate a Discretization function.

  • It allows you to use the algorithms from Automatic Discretization but in a more controlled environment where you can closely observe the results of the Discretization.

  • Click on the Generate a Discretization button.

  • Then, select the Type from the drop-down menu, e.g., the R2-GenOpt algorithm. You have nine algorithms available, i.e., the univariate methods only.

  • Choose the number of Intervals, e.g., 5.

  • Set a Minimum Interval Weight, which defines the minimum prior probability of an interval in percent. The default value is 1%.

  • Note that you can set defaults for the above settings under Main Menu > Window > Preferences > Discretization.

  • Additionally, there are options for Log Transformation and Isolate Zeros, which we discuss in the context of Automatic Discretization.

  • Click OK to perform the Discretization.

Workflow Illustration

Transfer the Discretization Thresholds

In certain situations, you may carefully choose thresholds for a variable (see Manual Discretization Workflow Animation). Perhaps another variable, or multiple variables, should have exactly the same discretization. In this context, you can use the Transfer the Discretization Thresholds button.

  • Select the source variable from which you wish to copy the thresholds.

  • Click the Transfer the Discretization Thresholds button.

  • A new window opens up that allows you to select one or more target variables.

  • Select the target variables.

  • Click OK.

Workflow Animation

Create a Class for Each Type of Discretization

  • This checkbox is synchronized across Manual and Automatic Discretization processes.

  • If checked, BayesiaLab automatically creates Classes for each type of Discretization, i.e., all variables that are discretized with the same algorithm will belong to the same Class.

  • Note that variables that were discretized manually, even if you used the Generate a Discretization button, will all become members of the Class MANUAL.

  • You can review the Class memberships in the Class Editor after the data import process is complete.

Load Discretizations

  • This function allows you to load a Discretization Dictionary with saved Discretization Intervals and Discretization Methods.

  • This approach is particularly helpful when you repeatedly import datasets with the same variables for which you have already found a suitable discretization.

The following text file illustrates the syntax of a Discretization Dictionary.

Discretization Dictionary

Age=R2_GENETIC_OPTIMIZATION 5 0.01
Weight\ (kg)=MANUAL 50 60 70 80 90 100 125 150 200
Standing\ Height\ (cm)=MANUAL 150 160 170 180 190
Body\ Mass\ Index=MANUAL 15 20 25 30 35 40
Waist\ Circumference\ (cm)=R2_GENETIC_OPTIMIZATION 5 0.01
Hip\ Circumference\ (cm)=R2_GENETIC_OPTIMIZATION 5 0.01
Upper\ Leg\ Length\ (cm)=R2_GENETIC_OPTIMIZATION 5 0.01
Upper\ Arm\ Length\ (cm)=R2_GENETIC_OPTIMIZATION 5 0.01
Arm\ Circumference\ (cm)=R2_GENETIC_OPTIMIZATION 5 0.01

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