Analysis Considerations in Industrial Split-Plot Experiments When the Responses are Non-Normal

1. Analysis Considerations in Industrial Split-Plot Experiments When the Responses are Non-Normal Timothy J. Robinson University of Wyoming Raymond H. Myers Virginia Tech Douglas C. Montgomery Arizona State

2. Mixture Experiment with Process Variables • Film manufacturing process • Three components, (X1,X2, X3), melted and mixed in a screw extruder to produce a roll of film • Pieces are cut from the roll and processed at a particular setting of the process variables • Response is a quality measure reflecting the amount of polarized light that passes through the film. • Response is distinctly non-normal and the coefficient of variation in the response is constant.

3. Thickness Gauge Inlet Winder ChillRolls Thermocouples Heaters Molten Polymer Web

4. Design Region At each mixture, a 23-1 design is run in the process variables. If design was completely randomized and we did not replicate, it would require 20 formulations of the mixture.

5. Whole Plots Mixture Variables Process Variables

6. Linear Mixed Model • Classical linear model when design is completely • randomized: • Cornell (1988) discusses a split-plot approach to the • analysis of mixture experiments containing process • variables.

7. - Many industrial experiments involve responses that are non-normal [Myers, Montgomery, Vining (2002)] • The generalized linear model (GLM) assumes that the mean is related to by a link function s such that: • The GLM assumes independent responses and • the variance/covariance matrix of responses is • given by: Generalized Linear Models where V is diagonal.

8. Generalized Linear Mixed Models • Just as linear models can be extended to linear mixed models, generalized linear models can be extended to generalized linear mixed models (GLMM). • GLMM’s are appropriate for split-plot experiments since they accommodate the inherent correlation among observations taken on the same whole plot. • Two types of GLMM’s considered: -Random effects GLMM -Covariance pattern GLMM

9. Model: is a diagonal matrix of the response given the random effect. denotes the conditional mean and plays the same role as y in the GLM. • Random effects GLMM: - whole plots treated as random effects and are modeled simultaneously with the regression coefficients • Regarding notation:

10. Random effects GLMM similar to linear mixed model except it accommodates a more general family of responses • Random effects enter the model in a non-linear fashion…consequently, predicted values from this model involve estimated random effects: • In film example, we have a gamma response with a log link and hence the prediction model is given by: • Predicted values are batch specific due to the random effects and is interpreted as the predicted quality for the ith batch.

11. Model: A is a diagonal matrix of variances determined by the distribution of the response with denoting correlation matrix of responses among observations in whole plot i. Split Plot  Designs • Covariance pattern GLMM: -regression of the response on the design variables is modeled separately from the within-whole plot correlation

12. Instead of using random effects to account for the correlation, a covariance structure is defined for the error term • Prediction equation for covariance pattern models is interpreted differently than the batch-specific interpretation resulting from random effects models In the film example, predicted values are determined from: and are interpreted as the average quality across all batches Covariance pattern models are often referred to as population-averaged models.

13. Fitting the Random Effects GLMM • Maximum likelihood where likelihood is determined from the product of the likelihoods of • With non-normal responses, solving the likelihood involves numerical integration techniques • A common approach to maximizing the likelihood proposed by Wolfinger and O’Connell is to use pseudo-likelihood • Pseudo-likelihood uses an iterative generalized least squares analysis of a pseudo-variable , which can be thought of as a linearized observation vector. • The pseudo-variable is a first order Taylor series expansion for and is given by

14. Recalling that we have • This model is similar to the classical linear mixed model and can be fit using the SAS macro GLIMMIX

15. Fitting the Covariance Pattern GLMM • Pseudo-likelihood is used to fit the covariance pattern model as well but with the following pseudo-variable: • The variation due to whole plots is treated as a nuisance and pooled into the error where and =

16. Batch-Specific vs. Population Averaged Prediction Models • In the random effects GLMM, the conditional expectation of y plays the same role as y in the traditional GLM • The quantity modeled in the random effects GLMM is and thus predicted quality is conditional on the ith batch • If one wished to have a prediction of quality across all batches, it would be reasonable to use since we typically assume

17. If average quality across all batches is of interest, one could model the unconditional expectation of y as is done in covariance pattern GLMMs • The covariance pattern model in the film example is .  • The difference between depends on the magnitude of . As a result, the predictions in the covariance pattern model are not dependent on an estimated random effect

18. Illustration • Consider the following random effects GLMM • Five random values for the are generated and profiles of what could be considered to be five batches are plotted. • For a prediction model across all batches, we could substitute • The true average profile could be determined by integrating out the as follows where we assume

19. Notice that is what is actually modeled with the covariance pattern GLMM • Evaluating the integral we have • Plots of the individual profiles, the approximate population averaged profile and the true population average profile are examined for

20. Long dashes denote and solid line denotes true population average profile

21. Long dashes denote and solid line denotes true population average profile

22. For the larger whole plot variance, at X=1, the true population average is approximately 55 whereas the estimated average is slightly less than 10. • When the whole plot variance is smaller, at X=1, the true population average is approximately 8 and the estimated population average is approximately 6 • Practical implication: If one is interested in predicting average quality across all batches, it is better to model the unconditional expectation of the response (covariance pattern GLMMs) than the conditional expectation (random effects GLMMs).

23. Analysis of Film Data • The following random effects and covariance pattern GLMMs were fit to the film data using the SAS macro GLIMMIX: • The mixture portion was considered to be first-order due to prior knowledge and only the X1X2 interaction was thought to be important. All mixture*process variable interactions were of interest except for those involving X3 Random Effects GLMM Covariance Pattern GLMM

24. 146.26 251.99 Contour plot of estimated population averaged model for film data using batch specific GLMM . 451.17 650.35 X1 X2 Contour plot of estimated population averaged model for film data using covariance pattern GLMM. 166.26 264.51 479.458 X1 X2

25. Contour plots of estimated response for individual batches 145.964 376.71 261.337 492.08 607.456 X1 X2 127.04 251.99 451.17 650.35 X1 X2 132.69 251.96 451.11 X1 X2

26. Conclusions • Non-normal responses are common in industry and the standard GLM analysis is inappropriate for experiments which are run as split-plots • In terms of prediction across all whole plot units, the population average model is most appropriate • The random effects model is useful in the sense of providing an estimate for the amount of whole plot variation in relation to the amount of variation among sub-plot units