OK thanks for inputs. Pity that stewise can't handle random effects, would be great; backward analysis is cumbersome when many factors are involved. Is the AICc criterion not OK for judging the models; the first model has 17 significant effect R² = 0,98 but AICc is much higher than the 2nd lower #effect model with R² = 0,9 ?
Thanks Cameron for this useful comment; I will have a look at this more or less forgotten DOE screening analysis platform. I agree that the fixed block approach consumes lot of degrees of freedom, on the other hand this will make sure that finally only strong effects will be screened out so I am happy with my lower but stonger effect R² = 0,9 model; I have tried the cumbersome standard least squares backward selection and sometimes it is hard to judge when to remove an effect or not; would you reject an interaction effect with p = 0,075? Using backward selection, nearly every time I find another model.. judgement of p is critical!! I prefer fixed effect/stepwise and indeed by creating the final model block must be transformed to a random effect. Regards, Frank
The screening platform is for two-level factors. It will model any factors with more than two levels (such as 9 blocks) as powers of fixed effects, up to the 8th power in this case, and not as random effects. You should not use the Screening platform in this case.
Also, this simple platform is for screening factors, not effects. It ignores your specified model to create contrasts based on the key principles of screening.
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