Shows or hides the desirability functions. Desirability is discussed in Desirability Profiling and Optimization.
Note: In many situations, the settings that optimize the desirability function are not unique. The Maximize Desirability option gives one such setting. The Contour Profiler is a good tool for finding alternative factor combinations that optimize desirability. For an example, see Explore Optimal Settings in Contour Profiler.
Figure 3.5 Maximization Options Window
Figure 3.6 Response Goal Window
Provides different approaches to calculating indices that measure the importance of factors to the model. These indices are independent of the model type and fitting method. Available only for continuous or binary responses. For details, see Assess Variable Importance.
(Available only when the Prediction Profiler is embedded in certain modeling platforms.) Launches the Bagging window. Bootstrap aggregating (bagging) enables you to create multiple training data sets by sampling with replacement from the original data. For each training set, a model is fit using the analysis platform, and predictions are made. The final prediction is a combination of the results from all of the models. This improves prediction performance by reducing the error from variance. For details, see Bagging.
Launches the Simulator. The Simulator enables you to create Monte Carlo simulations using random noise added to factors and predictions for the model. A typical use is to set fixed factors at their optimal settings, and uncontrolled factors and model noise to random values. You then find out the rate of responses outside the specification limits. For details, see the Simulator topic.
(Appears when a Sigma column property exists in any of the factor and response variables.) This option displays the 3σ interval that is implied on the response due to the variation in the factor. Propagation of error (POE) is important when attributing the variation of the response in terms of variation in the factor values when the factor values are not very controllable. See Propagation of Error Bars.
Shows or hides a purple triangle whose height and direction correspond to the value of the partial derivative of the profile function at its current value (see Figure 3.7). This is useful in large profiles to be able to quickly spot the sensitive cells.
Figure 3.7 Sensitivity Indicators
Displays a window for each factor enabling you to enter a specific value for the factor’s current setting, to lock that setting, and to control aspects of the grid. See the section Set or Lock Factor Values for details.
Figure 3.8 Factor Settings Window
Assigns the values of a data table row to the X variables in the Prediction Profiler.
{factor1 = n1, factor2 = n2, ...}
ProfileCallbackLog = Function({arg},show(arg));
Then enter ProfileCallbackLog in the Set Script dialog.
ProfileCallbackAssign = Function({arg},evalList(arg));
ProfileCallbackAccess = Function({arg},f1=arg["factor1"];f2=arg["factor2"]);
The prime reason to make uniform random factor tables is to explore the factor space in a multivariate way using graphical queries. This technique is called Filtered Monte Carlo.
Enables you to add, change, or delete linear constraints. The constraints are incorporated into the operation of Prediction Profiler. See Linear Constraints.