To use desirability profiling, select Desirability Functions from the Prediction Profiler red triangle menu.
This command appends a new row to the bottom of the plot matrix, dedicated to graphing desirability. The row has a plot for each factor showing its desirability trace, as illustrated in The Desirability Profiler. It also adds a column that has an adjustable desirability function for each Y variable. The overall desirability measure shows on a scale of zero to one at the left of the row of desirability traces.
As you drag these handles, the changing response value shows in the area labeled Desirability to the left of the plots. The dotted line is the response for the current factor settings. The overall desirability shows to the left of the row of desirability traces. Alternatively, you can select Set Desirabilities to enter specific values for the points.
Maximizing Desirability shows steps to create desirability settings.
The default desirability function setting is maximize (“higher is better”). The top function handle is positioned at the maximum Y value and aligned at the high desirability, close to 1. The bottom function handle is positioned at the minimum Y value and aligned at a low desirability, close to 0.
You can designate a target value as “best.” In this example, the middle function handle is positioned at Y = 70 and aligned with the maximum desirability of 1. Y becomes less desirable as its value approaches either 40 or 90. The top and bottom function handles at Y = 40 and Y = 90 are positioned at the minimum desirability close to 0.
Note: Dragging the top or bottom point of a maximize or minimize desirability function across the yvalue of the middle point results in the opposite point reflecting. A Minimize becomes a Maximize, and vice versa.
The last row of plots shows the desirability trace for each factor. The numerical value beside the word Desirability on the vertical axis is the geometric mean of the desirability measures. This row of plots shows both the current desirability and the trace of desirabilities that result from changing one factor at a time.
For example, Prediction Profile Plot with Adjusted Desirability and Factor Values shows desirability functions for two responses. You want to maximize ABRASION and MODULUS. The desirability plots indicate that you could increase the desirability by increasing any of the factors.
A desirability index becomes especially useful when there are multiple responses. The idea was pioneered by Derringer and Suich (1980), who give the following example. Suppose there are four responses, ABRASION, MODULUS, ELONG, and HARDNESS. Three factors, SILICA, SILANE, and SULFUR, were used in a central composite design.
The data are in the Tiretread.jmp table in the sample data folder. Use the RSM For 4 responses script in the data table, which defines a model for the four responses with a full quadratic response surface. The summary tables and effect information appear for all the responses, followed by the prediction profiler shown in Profiler for Multiple Responses before Optimization. The desirability functions are as follows:
1.

2.

ELONG target of 500 is most desirable.

3.

HARDNESS target of 67.5 is most desirable.

Select Maximize Desirability from the Prediction Profiler red triangle menu to maximize desirability. The results are shown in Profiler after Optimization. The desirability traces at the bottom decrease everywhere except the current values of the effects, which indicates that any further adjustment could decrease the overall desirability.