@DrThWillms wrote:
Hello,
ok, I admit the provocation was slightly volontary. On one side I saw and I see that the fold over principle is working also in case of the dsd given by JMP. However, on the other side, I wondered why some points could be the same whereas some points are different. I mean: I would have been less astonished if all points were exactly inversed. On the other side
As you describe, "some points are the same whereas some points are different" because conference matrices used for DSD have a specific property to respect (see Structure of Definitive Screening Designs) :
If you simply change the signs of a conference matrix, you wouldn't respect anymore this property: the product of the modified matrix by its transposed won't be equal to a multiple of the identity matrix. So the "modified" matrix won't be a conference matrix anymore.
Here is the result of the matrices multiplication using the conference matrix of order 6 from wikipedia article (and respecting the property seen before):
If you simply flip the signs from this conference matrix, you won't save the property of the original conference matrix :
So it may be more simple to imagine that flipping the signs would work, but it's false in this case, which may help you understand why you have different matrices, and the need of permutations in rows/columns to keep the property of conference matrices.
EDIT : I have made a typo in the example before, the matrix calculations are correct, but not the writing of the negative transpose of the conference matrix, a -1 is missing at row5, column 6.
Doing the matrix calculations with Numpy does provide the same results in the two cases here:
Case1 with conference matrix:
Case2 with negative conference matrix:
@DrThWillms wrote:
On the other side I wonder why JMP has the only program where everything is not the same as in other programs, which makes it difficult to compare - also see the randomisation theme . One time I asked if I can take a dsd from another program and use it in JMP for evaluation. I don't see anymore how this could be possible, if every,thing is so different. Why don't you just take the same values as in the article of JMP. if they are equivalent anyway? Perhaps this program is not as practicasl as I thought.
You can use a DSD or any other design from any program and use the Evaluate Designs or Compare Designs platforms to evaluate or compare the designs.
I don't see what's the problem behind the use of similar matrices ? This is exactly the same topic with fractional factorial design, except in this case you have the choice to create your generator and define your aliases. For DSD, this would imply selecting the conference matrix from a catalog of matrices, before creating the design. It may not be very practical for practitioners as it would involve several steps before creating the DSD, and this matrix choice might raise more questions than answers about which matrix to choose ; it would complicate the design creation process and lower the "user-friendliness".
If this is an obstacle for you, you can still use the matrix you're used to and import it in JMP.
I think you may have a different use of JMP than most of the users have, which could explain why you don't see it as practical as other users may find it.
Hope this complementary answer will help you,
PS : Please don't forget to kudos helpful answer and "Accept as a Solution" the answers that solve your initial questions (and the following ones) !
Victor GUILLER
"It is not unusual for a well-designed experiment to analyze itself" (Box, Hunter and Hunter)