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2^{K - p} Fractional Factorial Designs

Even smaller fractions for more sample efficient experiments.

Author

Affiliation

Kris Sankaran

 

Published

Nov. 23, 2021

DOI

Readings 8.3, 8.4, Rmarkdown

  1. 2K1 designs allow us to draw conclusions about K factors using only half of the runs that would be required for a full 2K factorial. It’s possible to generalize these ideas so that smaller fractions (14,18, of samples are required. When only a fraction 12p is required, we call the resulting design a 2Kp fractional factorial design.

  2. To construct a 2Kp design, we can follow a standard recipe,

  1. Here’s the recipe in action. Suppose we want a 262 design. This studies 6 factors using 16 (not 64) runs.
A B C D
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A B C D E = ABC F = BCD
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  1. Let’s analyze this design,
  1. For an exercise, you can try going through this process using an alternative confounding structure: Set E=ABCD, and F=ABC. It’s somewhat tedious, but will build your confidence with this type of design.

Code Example

  1. Let’s use the 262 design that we just constructed on a dataset about injection moldingAccording to Example 8.4 in the book, this is how paper clips are made. The 6 factors are,
# A tibble: 6 × 7
      A     B     C     D     E     F Shrinkage
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>     <dbl>
1    -1    -1    -1    -1    -1    -1         6
2     1    -1    -1    -1     1    -1        10
3    -1     1    -1    -1     1     1        32
4     1     1    -1    -1    -1     1        60
5    -1    -1     1    -1     1     1         4
6     1    -1     1    -1    -1     1        15
Visualizing a $2 ^ {6 - 2}$ design for the injection experiment. Each column is a factor, each row is a run.

Figure 1: Visualizing a 262 design for the injection experiment. Each column is a factor, each row is a run.

  1. We can look at the aliasing structure by inspecting the design matrix.

  1. Let’s make a Daniel plot and look at effects.

Based on the Daniel plot, it seems that a submodel based on just A, B, and AB should be sufficient (perhaps an argument could be made for ACD and AD, though).

            Df Sum Sq Mean Sq F value   Pr(>F)    
A            1    770     770   37.15 5.38e-05 ***
B            1   5077    5077  244.90 2.39e-09 ***
A:B          1    564     564   27.21 0.000216 ***
Residuals   12    249      21                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Both mold temperature and screw speed affect shrinkage. Moreover, there is an interaction; i.e., screw speed has a different effect on shrinkage depending on the underlying mold temperature.

  1. To evaluate the model, let’s visualize the residuals.

Notice that the residuals have lower variance at the setting C=1. Even though C doesn’t have a strong effect, that might be a useful fact, in case we want to select a configuration with the optimal response and low variability around that response.

Evaluating Designs

  1. In general, there will be several ways of constructing any 2p design. How do we know which one to use? Here are some criteria.

For example the example above had words ABCE,BCDF and ADEF and resolution 4, so aberration is 3.

Footnotes

  1. According to Example 8.4 in the book, this is how paper clips are made.[↩]