Effect Sizes
In complicated designed experiments, it is easiest to summarize the effect sizes with the model.tables function. This takes the name of the fitted model object as its first argument, and you can specify whether you want the standard errors (as you typically would):
model.tables(model1, "means", se = TRUE)
Tables of means Grand mean 3.851905 Water Water Tyne Wear 3.686 4.018 Detergent Detergent BrandA BrandB BrandC BrandD 3.885 4.010 3.955 3.558 Daphnia Daphnia Clone1 Clone2 Clone3 2.840 4.577 4.139 Water:Detergent Detergent Water BrandA BrandB BrandC BrandD Tyne 3.662 3.911 3.814 3.356 Wear 4.108 4.109 4.095 3.760 Water:Daphnia Daphnia Water Clone1 Clone2 Clone3 Tyne 2.868 3.806 4.383 Wear 2.812 5.348 3.894 Detergent:Daphnia Daphnia Detergent Clone1 Clone2 Clone3 BrandA 2.73 3.919 5.003 BrandB 2.929 4.403 4.698 BrandC 3.071 4.773 4.019 BrandD 2.627 5.214 2.834 Water:Detergent:Daphnia , , Daphnia = Clone1 Detergent Water BrandA BrandB BrandC BrandD Tyne 2.811 2.776 3.288 2.597 Wear 2.653 3.082 2.855 2.656 , , Daphnia = Clone2 Detergent Water BrandA BrandB BrandC BrandD Tyne 3.308 4.191 3.621 4.106 Wear 4.530 4.615 5.925 6.322 , , Daphnia = Clone3
Detergent Water BrandA BrandB BrandC BrandD Tyne 4.867 4.766 4.535 3.366 Wear 5.140 4.630 3.504 2.303 Standard errors for differences of means Water Detergent Daphnia Water:Detergent Water:Daphnia 0.1967 0.2782 0.2409 0.3934 0.3407 replic. 36 18 24 9 12 Detergent:Daphnia Water:Detergent:Daphnia 0.4818 0.6814 replic. 6 3
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