DoE with Blocking and Constraints

[#1]

I have to set up a DoE with blocking and Constraints. JMP pops up with "Constraint scripts are not supported for experiments with Blocking  or mixture factors".

Does anyone of you have an Idea how I can solve my problem anyhow.

Just some background: I have Discrete Numeric and Categorial Factors in the experiment. Blocking is needed because I have 16 Teststations involved. I have a low end level combination of two Discrete Numeric 3Level Factors, that will not function- "Use disallowed combination Filter" works fine if I do it without blocking.

Many thanks for every help Peter

I prefer a interactive solution, but JSL would work also, but please with comments I'm just starter.

[#2]

@peter_michel, you mentioned a screen capture but there's no image in your post above. Can you edit your post and include it?

[#3]

Hi Jeff,

this post was refering to a post above. Sorry for creating confusion. Underneath it is now complete. Mark was asking for the meaning of the constraints.

Hi Mark,

here in this screen dump, I have added senseless constraints, just to show: as soon as I add a constraint I will get the failure message shown underneath.

Did you get a failure message as well after changing from "specify linear constraints" to "Use dissallowed combination Filter" and click make table?

Peter

I would like to correct the post as you pleased me,  but there is no button to edit. May be you can guide me that we clean up the postings for other readers.

Peter

[#4]

I don't see a picture of your screen.

I did not use the disallowed combinations feature to define a constraint because you proved that this feature won't work with blocking factors. I hoped that your constraint might be defined as a linear constraint instead, but you haven't answered my questions yet.

[#5]

Hi Mark,

sorry I did not understood your hint,         to realize my constraints by "Specify linear constraints" tool/function. I will try.

To answer your question now.

It will not work:

Small cross section with agent 1 or 2

High spring force  with  Agent 1 or 2

I added also the factors in   .JMP

Thanks Peter

[#6]

You only have two levels for Agent (Agent1, Agent2). I understand the constraint now to be such that (1) you cannot use a low cross section with either Agent and (2) you cannot use a high spring force with either Agent. So why not limit the individual factor ranges instead of trying to introduce a constraint?

Also, why are you using the discrete numeric factor type for the first four factors instead of the continuos factor type?

[#7]

Sorry I did not explain that in detail.

There are two materials that can be used. And both of the materials can be improved by an Agent. This Agent is varied with 1% or 2 % (Certainly all these numbers are anonymized). Hence I have 0% --> no Agent, or 1% or 2 % Agent.

I use "Discrete Numeric" Factors because the processes of our supplier who is driving this DoE in his facilities, are not able to adjust the Factors to continuous values that are  chosen by optimally criterias.

Sorry my impression is we deviating from my question. Let me raise my originally question again. What is the difference between.

Blocking Factor with 16 Blocks or Categorical Factor with 16 Levels. See underneath. Sure, making Amout of levels equally and adding just a main effect for the categorical value.

 

You answered above: "It is a common misconception that a blocking factor (blocks have fixed effects on the response) is just another categorical factor but they are much more than that. They represent a constraint on the experimental unit (part of the experimental run but generally not explicitly part of the design except for blocking)".

What does it mean? What causes the difference? What is the failure I have to expect if I choose an optimal design with a categorical Value instead of Blocking?

Many thanks

Peter 

[#8 · Accepted solution]

The purpose of a categorical factor is to model its fixed effects (estimate and test). The purpose of a blocking factor is to provide a homogeneous group of runs based on the inclusion of an external variable (e.g., tester) in the design. The homogeneity provides for estimation or comparison without the additional variation from changing the blocks. Full randomization of the runs across blocks would not necessarily provide the best set of treatments in each block. Full randomization occurs with a categorical factor.

You would not include this variable if you only needed one level, but you have 16 levels out of convenience or necessity. You are not interested in the effect of the blocks but you want the groups to be homogeneous and you want to be able to account for their variance.

[#9]

Hi Mark,

that makes it more clear. Thanks

Peter