topic Re: Modeling Split Plot DOE results in Discussions

Modeling Split Plot DOE results

frankderuyck — Wed, 20 May 2026 16:07:11 GMT

Generalized regression does not work with random effects like whole plot factors so, as random noise cannnot be removed, guess genreg can't be used to model split plot DOE results ?

Re: Modeling Split Plot DOE results

statman — Wed, 20 May 2026 22:13:43 GMT

I suggest you read:

Box, G.E.P., Stephen Jones (1992), “Split-plot designs for robust product experimentation”, Journal of Applied Statistics, Vol. 19, No. 1

Re: Modeling Split Plot DOE results

frankderuyck — Thu, 21 May 2026 06:00:19 GMT

I don't find a free version of this paper. JMP GENREG in 1992??

Re: Modeling Split Plot DOE results

Victor_G — Thu, 21 May 2026 07:52:58 GMT

Hi @frankderuyck,

You can fit Generalized Regression models with random effects by choosing the Generalized Linear Mixed Model personality when launching the Fit Model platform (JMP Pro):

Hope this quick and practical answer will help you,

Re: Modeling Split Plot DOE results

frankderuyck — Thu, 21 May 2026 08:13:24 GMT

I know this Victor but it does not have this great Forward selection Aicc/penalize options like GENREG. Is there anywhere an "All Subsets" option available to get the best model?

Re: Modeling Split Plot DOE results

frankderuyck — Thu, 21 May 2026 08:41:32 GMT

Maybe start with GENREG without whole plot factor and select the stong effects using "Best Subset" then return to Fit Mixed with the strong effects + random whole plot?

Re: Modeling Split Plot DOE results

Victor_G — Thu, 21 May 2026 08:43:32 GMT

No this option is not available, and is not recommended in DOE scenarii. The "All Subsets" option is more a data-mining, model-agnostic way of building models. If you remove the whole plot factor to use the GenReg platform, you'll lose the advantage of having a random effect, capturing the variance you are not interested in. Without the random effect, this variance will be considered as random, and may "hide" potentially active effects.

When building the DoE, you have assumed a model. Fit this model with the random effect(s) through the Generalized Linear Mixed Model, and evaluate the relevance and correctness of this model (residual analysis, model's metrics, etc...).
You can then start to relaunch the model by removing some higher order terms (but still respecting effect heredity), and compare the different models you have : factors influences on the response(s), models metrics, residual analysis...

Re: Modeling Split Plot DOE results

frankderuyck — Thu, 21 May 2026 08:54:31 GMT

Or even better - as with whole plot random effect, factor levels can get correlated - start with genreg elastic net to select strongest effect first?

Re: Modeling Split Plot DOE results

Victor_G — Thu, 21 May 2026 09:01:30 GMT

Same problem: since you won't use the random effect, part of the variance will be considered as random. So you'll end up with having more difficulty to detect effects, no matter the method used

Re: Modeling Split Plot DOE results

frankderuyck — Thu, 21 May 2026 09:31:12 GMT

Thanks for interesting reply Victor, you have a point, without the random whole plot effect there will be significant noise however strong effects will peak more out of the noise. In the "best subset" result you shoud not be too severe on P-values, think a threshold of 0,1 is ok for selecting factors with stongest effect.