JMP Blog

Victor_G · Nov 13, 2025 09:17 AM

Introduction

Welcome back to our space filling DOE series! In Part 1, we explored the fundamentals and common types of space filling designs. Now we dive into the heart of our comparative study: establishing a robust framework to evaluate and compare these designs across different scenarios.

The comparative study framework

To fairly assess space filling designs, we need standardized conditions and meaningful metrics. Our approach evaluates designs across multiple dimensions of performance.

Responses

Space coverage metric/uniformity response

Discrepancy: For measuring deviation from theoretical uniform distribution. The goal is to minimize the difference between the distribution generated by the design and a uniform distribution for each factor. Discrepancy response allows the space coverage and uniformity of the design points repartition to be evaluated: the lower the discrepancy, the more uniform the repartition of the design points (and the better the coverage of the experimental space).

Once the factors have been entered in the Space Filling platform and the type of design has been chosen, the discrepancy value can be found in the Design Diagnostics panel (Figure 1).

Figure 1: Space filling design panel. Design diagnostic of a fast flexible filling design for two factors with 20 runs. The discrepancy (red) and MaxPro (blue) values are displayed after design generation in the Design Diagnostics panel.

To better understand the discrepancy value and how it relates to the distances between design points, let’s compare two space filling designs - Latin hypercube and fast flexible filling - created for six factors with 60 runs each (Figure 2).

Figure 2: Visualization of the minimum distance between points for a fast flexible filling and a Latin hypercube design.

If we compare the minimum distance between points for the two designs, we can clearly see that the Latin hypercube design provide a very narrow distribution of values for the minimum distance between design points. This behavior shows the better discrepancy of the Latin hypercube design over the fast flexible filling design for this specific dimensionality and number of points, where the design points are uniformly distributed, resulting in a stable minimum distance between points.

Projection property response

MaxPro: Evaluates space filling properties on projections to all possible subsets of factors. The goal is to maximize the MaxPro criterion to ensure good projections of design points in lower dimensional spaces.

Once the factors have been entered in the Space Filling platform and the type of design has been chosen, the MaxPro value can be found in the Design Diagnostics panel (Figure 1).
The MaxPro statistic is undefined for sphere packing design because points can have identical values for one dimension.

To better understand the MaxPro value and how it relates to the projection property of designs, let’s compare two space filling designs - Latin hypercube and fast flexible filling - created for six factors with 60 runs each.
If we project the design points of these two space filling designs in two dimensions, we can compare differences in projection (Figure 3).

Figure 3: Visualization of projection property of two space filling designs with six factors and 60 runs. Note how the Waern links connect all design points in the Latin hypercube scenario based on their actual proximities, but not for the fast flexible filling design.

For the Latin hypercube design on the left, distances between design points are homogeneous, and every design point is connected to the others, showing homogeneous repartition of points and good projection properties for this design situation. For the fast flexible filling design on the right, distances between design points are heterogeneous, and connexion between design points are different.

Discrepancy and MaxPro values are heavily correlated; a design with very low discrepancy will exhibit a homogeneous and uniform repartition of points, facilitating the projection of the design points into smaller subspace.

Design generation complexity

HP Time(): The function HP Time() returns a high precision time value in microseconds. Since this function is only useful when used relative to another HP Time() value, it is included in the design generation script. The example here creates a sphere packing design with 60 runs for three factors, and the recording and display of the generation time is a result of the two HP Time() functions:
```
t1 = HP Time();

DOE(
   Space Filling Design,
   {Add Response( Maximize, "Y", ., ., . ),
   Add Factor( Continuous, -1, 1, "X1", 0 ),
   Add Factor( Continuous, -1, 1, "X2", 0 ),
   Add Factor( Continuous, -1, 1, "X3", 0 ),
   Set Random Seed( 138892707 ),
   Space Filling Design Type( Sphere Packing, 60 ), Simulate Responses( 0 ),
   Set Run Order( Randomize ), Make Table}
);

t2 = HP Time();

Show( t2 - t1 );
```
The generation time (t2-t1) can be seen in the log (Figure 4).

Figure 4: Log result of the script with the display of generation time.

A transformed response column Time (s) is used for the response, as the units (seconds) are much more understandable and comparable between designs and enable the design generation time to be assessed more easily. The column Time (s) is created with the following formula:
```
Time (s) = HP Time (µs) / 1000000
```

Factors

As we want to cover the biggest differences in space filling design generations, we need to specify different factors that will help create diverse designs in terms of design generation methods, dimensionality, and density of points.

Design type: As already seen in the first post in this series, different design generation methods exist for space filling designs, with different distributional or geometrical emphasis for different use cases and benefits. In the context of this study, we explore the most common type of methods to generate space filling designs, using uniform, sphere-packing, Latin hypercube, and fast flexible filling designs.
Dimensionality (number of factors): As the dimensionality (number of factors) increase, the performance of space filling designs may decrease, as it becomes more complex and time-consuming to maintain a uniform distribution for each factor and good projection properties. To study the impact of dimensionality on the different responses listed, we study the dimensionality aspect through three levels:

Low-dimensional: Three factors
Medium-dimensional: Six factors
High-dimensional: Nine factors

Sample size variations (number of runs/factors): By default, JMP provides a default ratio of number of runs per factor that is equal to 10, enabling good design performance and ensuring easy modeling. However, depending on the experimental budget allowed, the performance of space filling designs can greatly vary, as it can becomes complex to maintain a uniform distribution for each factor or good projection properties for low number of design points. To account for this factor, we test multiple sample sizes:
- Small: Three designs points per factor, a situation that is similar to what can be obtained with classical central composite designs (CCD), where the repartition of points is done so that each factor is tested at its minimum, middle and maximum values.
- Medium: Twenty design points per factor, close to the JMP default ratio of 10 design points per factor.
- Large: One hundred design points per factor, corresponding to the need for building highly predictive models, thanks to a high density of points in the design space.

Experimental procedure: Design choice

We want to use a very simple design that will help compare the different factor levels easily. Moreover, as the design generation seed is not fixed, we want to estimate algorithm variability and separate this aleatoric variability from any factor effects. Also some combinations could be challenging to realize for certain designs configurations, so we want a design that enables a robust analysis even in the presence of possible outliers or missing values.

For all these reasons, a full factorial design is chosen. To create a full factorial design in JMP, the path is easy and straightforward: in the DOE menu, choose Classical and then Full Factorial Design (Figure 5).

Figure 5: DOE menu for creating a full factorial design.

Following the responses and factors definition that were listed earlier, we can then create our design to evaluate and compare different space filling configurations (Figure 6).

Figure 6: Creating a full factorial design to evaluate and compare different space filling scenarios.

Since a full factorial design can perform all possible factor level combinations, 36 runs are needed (4x3x3). The script to generate the full factorial design is:

DOE(
	Full Factorial Design,
	{Add Response( Minimize, "Discrepancy", ., ., . ),
	Add Response( Maximize, "MaxPro", ., ., . ),
	Add Response( Minimize, "Time", ., ., ., ., ., "s" ),
	Add Factor(
		Categorical,
		{"Fast Flexible", "Uniform", "Sphere Packing", "Latin Hypercube"},
		"Type",
		0
	), Add Factor( Continuous, {3, 6, 9}, "Factors", 0 ),
	Add Factor( Continuous, {3, 20, 100}, "Ratio exp/factors", 0 ),
	Set Random Seed( 31068506 ), Make Design, Simulate Responses( 0 ),
	Set Run Order( Randomize ), Make Table}
);

The data table obtained with this script can be seen in Figure 7.

Figure 7: Full factorial design table

Response evaluation

For each row in the design, a space filling design is created with the corresponding parameter values specified: number of continuous factors, number of points per factor, and design type. Discrepancy and MaxPro metric values are recorded for all design configurations.

Analysis framework

To analyze the results of this comprehensive study on space filling designs, two approaches are considered:

Ranking analysis approach: To simplify the comparison of the performance of the space filling design type across different configurations (dimensionality and sample size) for the three responses, a ranking is done, from 1 (best design) to 4 (worst design).

For responses where the goal is to minimize the value, such as discrepancy and time, the script used involves the function Col Rank, which returns the rank ranging from 1 as lowest. The <<Tie argument is set to “average”, in case two design configurations have the same performance, to return the average of tied ranks. The By variables used are “Factors” and “Ratio exp/factors” to rank the different design types for each dimensionality and sample size scenarios.
- Formula for discrepancy ranking:
```
Col Rank( :Discrepancy, :Factors, :"Ratio exp/factors"n, <<Tie( "average" ) )
```
- Formula for time ranking:
```
Col Rank( :Time, :Factors, :"Ratio exp/factors"n, <<Tie( "average" ) )
```
- Formula for MaxPro ranking:
  For MaxPro ranking, the order needs to be reversed: the designs with the higher MaxPro values are the best performing ones for this evaluation. As four different design types are evaluated for each scenario, the formula is modified so that the best ranking is still the first. The <<Tie argument is changed to “min” in case of tied ranks and the same By variables are specified.
```
5 - Col Rank( !Is Missing( :MaxPro ), :Factors, :"Ratio exp/factors"n, <<Tie( "min" ) )
```

Modeling approach: A modeling approach is done through the use of the model script attached in the data table after design generation. As the study design is a full factorial design, the default a priori model specified in this script involves all main effects (design type, number of factors and ratio of experiments per factor) and all two-factor interactions (Figure 8).

Figure 8: Default Fit Model launch. The option Fit Separately is checked to fit separate models for each response variables.

As three levels are used for the continuous factors, the modeling script is modified to account for curvature effects due to quadratic effects of the continuous factors (Figure 9).
Figure 9: Modified Fit Model launch. The model script is modified by adding quadratic effects for continuous factors. Select the two continuous factors and then click on Macros>Polynomial to Degree to add the two quadratic effects.

The model used to analyze the performance responses of the space filling designs includes main effects, two-factor interactions, and quadratic effects for the continuous factors.

Coming next

In our final post, we reveal the results of this comprehensive comparison! We show which space filling designs excel in different scenarios and provide practical guidelines for design selection based on experimental objectives and constraints.

This is the second part of a three-part series on space filling designs of experiments:

Introduction to common types of space filling designs.
Comparative framework and evaluation metrics (this post).
Results, analysis, and design selection guidelines (coming soon).