Turn on suggestions

Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

- JMP User Community
- :
- Discussions
- :
- Discussions
- :
- Retrospectively determine matrix design?

Topic Options

- Start Article
- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Apr 11, 2016 3:27 PM
(5270 views)

How best to try to retrospectively determine what factors an experimental matrix was designed to evaluate?

I have a block of data that were dumped off on me that presumably came from a fractional factorial type experimental matrix. I have the responses and I have the levels of the various parameters that were varied for the matrix. What I don't have is what terms were targeted in the original design.

Any help is appreciated.

Thanks!

1 ACCEPTED SOLUTION

Accepted Solutions

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

If you load your design into JMP, you can go to DOE->Evaluate Design, and pick your 6 columns for factors. It pre-loads the main effects for your model, with all the 2-factor interactions as alias terms (ie., not in the model, but potentially active). Examining the Color Map on Correlations, I can see it's a regular resolution IV fractional factorial with 3 center runs. To see which interactions are aliased you can hover over the off-diagonal entries that are red.

Cheers,

Ryan

7 REPLIES 7

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Re: Retrospectively determine matrix design?

Assuming that you received the data in the JMP data table created from the JMP DOE, you can then click on the DOE Dialog script in the Tables Panel, and it will return you back to the design dialog.

Jim

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Re: Retrospectively determine matrix design?

Unfortunately it was sent to me in Excel.

I don't know what software package was used and the organization that the data came from isn't very responsive so I was hoping I could piece it together.

Thanks for the reply!

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Re: Retrospectively determine matrix design?

Stating the obvious perhaps, but a factor at n levels allows terms in the model up to (n-1) in that factor. So looking at the number of unique values in each column describing how a factor was changed should help. You could also try using 'Columns > Recode' to make the design easier to read using integers for levels. If you post that here, somebody might recognise it.

But, by definition, a fractional factorial design always involves some confounding relative to the full factorial (which allows all terms to be unambiguously estimated). So, even if you 'did' know the logic behind the design you have, it will still support multiple models which you have to somehow disambiguate between (often using sparsity of effect or heredity arguments). Even without this knowledge you may still be able to build a plausible model, though how 'plausible' will depend on what you actually want to use the model for.

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Re: Retrospectively determine matrix design?

Below is the pattern of factors that was used. We have 6 factors at essentially two levels with three center points sprinkled in. I don't see any replicates, save the three center point runs. This design shouldn't be capable of detecting curvature. I was mostly interested in trying to pry out the interactions that are resolvable and knowing the confounding pattern...

Thanks for your help!

Factor A | Factor B | Factor C | Factor D | Factor E | Factor F |

-1 | -1 | 1 | 1 | -1 | 1 |

-1 | 1 | 1 | 1 | -1 | -1 |

0 | 0 | 0 | 0 | 0 | 0 |

1 | -1 | 1 | -1 | -1 | -1 |

1 | 1 | 1 | 1 | 1 | 1 |

1 | -1 | 1 | 1 | 1 | -1 |

-1 | 1 | -1 | 1 | 1 | 1 |

0 | 0 | 0 | 0 | 0 | 0 |

1 | -1 | -1 | -1 | 1 | 1 |

-1 | -1 | 1 | -1 | 1 | 1 |

1 | 1 | -1 | -1 | 1 | -1 |

1 | 1 | 1 | -1 | -1 | 1 |

1 | 1 | -1 | 1 | -1 | -1 |

1 | -1 | -1 | 1 | -1 | 1 |

-1 | -1 | -1 | 1 | 1 | -1 |

-1 | 1 | -1 | -1 | -1 | 1 |

-1 | 1 | 1 | -1 | 1 | -1 |

-1 | -1 | -1 | -1 | -1 | -1 |

0 | 0 | 0 | 0 | 0 | 0 |

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

If you load your design into JMP, you can go to DOE->Evaluate Design, and pick your 6 columns for factors. It pre-loads the main effects for your model, with all the 2-factor interactions as alias terms (ie., not in the model, but potentially active). Examining the Color Map on Correlations, I can see it's a regular resolution IV fractional factorial with 3 center runs. To see which interactions are aliased you can hover over the off-diagonal entries that are red.

Cheers,

Ryan

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Re: Retrospectively determine matrix design?

Nice!

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Re: Retrospectively determine matrix design?

Beautiful.

I didn't know that could be done.

Thank you!