Suppose you are trying to estimate the effect that 6 factors have on a response, and you know that none of the factors influence the effect of the others, so that a simple model like this
(1) |
is the perfect choice. How should you get the data you need to estimate the ’s? You may be tempted to design a test to estimate each of these factors by changing one factor at a time (OFAT). There are no interaction terms (e.g. ) in equation 1. So there’s no need to perform any runs that change several of the ’s at once, right? Wrong.
Table 2 shows a 36 run OFAT design. There are three repeated cases for each treatment. Table 1 shows a 32 run D-optimal design. There are no repeated runs. You might expect that you would be better able to estimate error from the design in Table 2 because of replication, but you’d be wrong. In fact, as Figure 1 shows,
the average standard error in the coefficient estimates for the model in equation 1 are significantly lower for the D-optimal design most of the time even with fewer runs than the OFAT design.
Why does this happen? Each run in the D-optimal design contributes to the estimate of every term in the model. However, each run in the OFAT design can only contribute to the estimate of a single term in the model. The “error bars” for OFAT designs will almost always be significantly larger than D-optimal designs (other optimality criteria give largely the same improvement over OFAT in practice).
X1 | X2 | X3 | X4 | X5 | X6 | |
1 | -1 | -1 | -1 | -1 | -1 | -1 |
2 | 1 | -1 | -1 | -1 | -1 | -1 |
3 | -1 | 1 | -1 | -1 | -1 | -1 |
5 | -1 | -1 | 1 | -1 | -1 | -1 |
8 | 1 | 1 | 1 | -1 | -1 | -1 |
9 | -1 | -1 | -1 | 1 | -1 | -1 |
12 | 1 | 1 | -1 | 1 | -1 | -1 |
14 | 1 | -1 | 1 | 1 | -1 | -1 |
15 | -1 | 1 | 1 | 1 | -1 | -1 |
17 | -1 | -1 | -1 | -1 | 1 | -1 |
20 | 1 | 1 | -1 | -1 | 1 | -1 |
22 | 1 | -1 | 1 | -1 | 1 | -1 |
23 | -1 | 1 | 1 | -1 | 1 | -1 |
26 | 1 | -1 | -1 | 1 | 1 | -1 |
27 | -1 | 1 | -1 | 1 | 1 | -1 |
29 | -1 | -1 | 1 | 1 | 1 | -1 |
32 | 1 | 1 | 1 | 1 | 1 | -1 |
34 | 1 | -1 | -1 | -1 | -1 | 1 |
35 | -1 | 1 | -1 | -1 | -1 | 1 |
37 | -1 | -1 | 1 | -1 | -1 | 1 |
40 | 1 | 1 | 1 | -1 | -1 | 1 |
41 | -1 | -1 | -1 | 1 | -1 | 1 |
44 | 1 | 1 | -1 | 1 | -1 | 1 |
48 | 1 | 1 | 1 | 1 | -1 | 1 |
49 | -1 | -1 | -1 | -1 | 1 | 1 |
54 | 1 | -1 | 1 | -1 | 1 | 1 |
55 | -1 | 1 | 1 | -1 | 1 | 1 |
56 | 1 | 1 | 1 | -1 | 1 | 1 |
59 | -1 | 1 | -1 | 1 | 1 | 1 |
60 | 1 | 1 | -1 | 1 | 1 | 1 |
62 | 1 | -1 | 1 | 1 | 1 | 1 |
63 | -1 | 1 | 1 | 1 | 1 | 1 |
X1 | X2 | X3 | X4 | X5 | X6 | |
1 | 1 | 0 | 0 | 0 | 0 | 0 |
2 | 0 | 1 | 0 | 0 | 0 | 0 |
3 | 0 | 0 | 1 | 0 | 0 | 0 |
4 | 0 | 0 | 0 | 1 | 0 | 0 |
5 | 0 | 0 | 0 | 0 | 1 | 0 |
6 | 0 | 0 | 0 | 0 | 0 | 1 |
7 | -1 | -0 | -0 | -0 | -0 | -0 |
8 | -0 | -1 | -0 | -0 | -0 | -0 |
9 | -0 | -0 | -1 | -0 | -0 | -0 |
10 | -0 | -0 | -0 | -1 | -0 | -0 |
11 | -0 | -0 | -0 | -0 | -1 | -0 |
12 | -0 | -0 | -0 | -0 | -0 | -1 |
13 | 1 | 0 | 0 | 0 | 0 | 0 |
14 | 0 | 1 | 0 | 0 | 0 | 0 |
15 | 0 | 0 | 1 | 0 | 0 | 0 |
16 | 0 | 0 | 0 | 1 | 0 | 0 |
17 | 0 | 0 | 0 | 0 | 1 | 0 |
18 | 0 | 0 | 0 | 0 | 0 | 1 |
19 | -1 | -0 | -0 | -0 | -0 | -0 |
20 | -0 | -1 | -0 | -0 | -0 | -0 |
21 | -0 | -0 | -1 | -0 | -0 | -0 |
22 | -0 | -0 | -0 | -1 | -0 | -0 |
23 | -0 | -0 | -0 | -0 | -1 | -0 |
24 | -0 | -0 | -0 | -0 | -0 | -1 |
25 | 1 | 0 | 0 | 0 | 0 | 0 |
26 | 0 | 1 | 0 | 0 | 0 | 0 |
27 | 0 | 0 | 1 | 0 | 0 | 0 |
28 | 0 | 0 | 0 | 1 | 0 | 0 |
29 | 0 | 0 | 0 | 0 | 1 | 0 |
30 | 0 | 0 | 0 | 0 | 0 | 1 |
31 | -1 | -0 | -0 | -0 | -0 | -0 |
32 | -0 | -1 | -0 | -0 | -0 | -0 |
33 | -0 | -0 | -1 | -0 | -0 | -0 |
34 | -0 | -0 | -0 | -1 | -0 | -0 |
35 | -0 | -0 | -0 | -0 | -1 | -0 |
36 | -0 | -0 | -0 | -0 | -0 | -1 |
Hello VC, I am a bit confused by the topic of the post (and also I am not good with R). Basic question: in the table 1 shouldn't these be 0 and 1, not -1 and 1?
ReplyDeleteIt's common practice to center the factor levels so a two-level factor takes values -1 and 1.
DeleteWith the AlgDesign function gen.factorial used in the script above you can change this with the 'center' option (center=FALSE instead of center=TRUE).
My goal with the post certainly wasn't to confuse, so please ask more questions if you've got them. Anything in particular that is especially confusing?