Could Screening design used for 1 continuous factor, 7 other categorical factors, and 1 categorical response?

[#1]

I would like to evaluate the influence of 8 factors on the response. There are 1 continuous factor, 7 other categorical factors, and 1 categorical response, and 2 of the categorical factors have 4 levels, one of the categorical factors has 8 levels. Can we use the classic screening design method to design experiments?

We want to know the most influential factors among the 8 factors, where we could analyze the screening experimental results? When we add more than 6 factors or the levels of factors exceed 3, the "Screening" tab disappeared where we cannot analyze the screening design results. What are the problems? Does that mean the classical screening design cannot be used in this case? Or we should analyze the screening design results separately? 

Thanks.

[#2]

Once you find yourself in a situation where the levels of factors is anything other than 2, the classic screening designs, that is, two level fractional factorial designs are not applicable or even doable. In your case with some factors having levels other than 2 you are forced into using an optimal DOE scenario...which in JMP takes you straight to the Custom Design platform. Since you seem to want to run a screening design I would use the Custom Design platform to build a design that is supported by a main effects only model. With an eye towards minimizing the number of runs. You may want to add a few runs over and above the JMP specified minimum to allow for some degrees of freedom for error.

Good luck trying to find active effects with a categorical response...my past experience is sometimes you need lots of runs to tease a signal from the noise...but you'll never know unless you try.

[#3]

Thank you very much. And I have another follow-up question.
I used the Custom Design platform. Since I have a binary response, when I fit the model, I chose Personality: Generalized Regression, Distribution: Binomial, Estimation Method: Elastic Net -- Adaptive, Validation Method: AICc. But when I click on the "Go" button, it shows an alert which is "Fitting terminated early because of failure to maintain heredity. Consider running again without enforcing heredity". Is this a severe problem I have to solve, how could I solve the problem, or should I choose some other methods?

[#4]

This refers to the fact that the model hierarchy is important. JMP strongly encourages you to maintain the hierarchy, but does not require it.

Model hierarchy means that if you have X1*X2 in the model, then X1 and X2 should also be in the model, even if they are not significant alone.

[#5]

@yiyichu Great that you used the Custom Design platform. Can you share the specific model you articulated for the design construction? Was it main effects only? Or something different?

In my first reply, I suggested you specify a main effects only model...the error message you are seeing and based on @Mark_Bailey 's correct reply, it sure looks like you are trying to fit a model with more than just main effects? Does your design support at least some level of estimation for at least some two (or heaven forbid) even higher order factor interactions? With categorical factors at more than 2 levels, to estimate interaction effects containing those factors your design can get big in a hurry. Especially with the one factor with 8 levels.

And I'm also just a bit curious regarding why you chose the Elastic Net fitting personality? I guess, in a screening modeling mode, I might have started out with perhaps just a simple nominal logistic regression model. Elastic Net isn't an incorrect choice...but it might tend to zero out some of the parameter estimates since that's it's very nature and you might miss effect level information content. Just curious more than anything?

[#6]

I am also curious, but I'm curious why you are looking at 4 level and 8 level categorical factors at all.  This sounds more like a pick the winner test than a screening design?  My thoughts: Screening is not meant to test all possible combinations.  Can you pick the extremes of the 4 or 8 levels?  The idea would be to compare all of the factors (equally, without bias) and determine which of those are worthy of further investigation.  By testing at more than 2 levels for some of your factors, you have more information about those factors and have therefore biased your study.

But, of course, I don't know your specific situation, so you may want to ignore me.

[#7]

@statman The 4-level factor here is "motion level", including Major motion, Minor motion, Fine motion, and none motion. The 8-level factor here is to randomly choose 8 different locations in a room for our case. But you are right. Actually, I am also considering picking the extremes of the 4 level and 8 level factors to make all the factors at 2 levels. Then we may also choose the definitive screening design method. I am just not sure which way could be better. Now your advice makes me more confident to do it this way. Thank you.

[#8]

For Motion, pick extremes.  If that is significant, then you can "fine tune".  For location in room...Can you actually control this or is it noise? If noise, consider blocking.

[#9]

@statman Generally, our goal is to decide whether the location of occupants in a room would influence the performance of occupancy sensors to detect occupants. So we would mark a 3x3 ft grid on the floor first, and randomly choose 8 locations using some random number generation tools, and occupants will stand in these 8 different locations for testing. Is this kind of like we could control the location? Actually, since we know that the sensor may not detect occupants when one person is standing at the corner of the room, does it mean we could also choose the peak extremes for the location? For example, one peak extreme is the person standing in the center of the room so that the sensor has the largest probability to detect occupants, the other would be the person standing in the corner of the room so that the sensor has the smallest probability to detect occupants?

[#10]

In this case, location in the room is noise. You have no control over where someone will be in the room yet you still want your sensor to detect they are there. The location in the room CAN be controlled during the experiment, but not in real life. This type of factor can be handled a number of different ways:
1. Repeats: Keep the treatment combination constant and take repeated measures at different locations. The locations can be random, but you will get more information if there are specific hypotheses about WHY there would be differences in the locations (e.g., proximity to sensor, angle from sensor, corner of room) and therefore sample systematically.
2. Randomized replicates: I believe this is how you are handling this factor. While this increases inference space and provides a theoretically unbiased estimate of the MSE, you don't know the effect of the location and you may compromise design factor effect detection precision.
3. RCBD: In this case, you would select best location (close right in front of the sensor) as 1 level and worst location (far and at an extreme angle) as a 2nd level. Replicate the treatments over the 2 blocks. In this case you could treat the location as a fixed effect. This allows for increased inference space, as well as the ability to estimate the Block (location) and all block-by-factor interaction effects (a measure of robustness of your sensor) with increased precision.
4. Split-plot (cross product array): Either put the treatments in the whole plot and noise in the subplot or noise in the WP and treatments in the SP. This would improve the efficiency of the design and likely increase precision of detecting design factor and noise by factor interaction effects.