June 2007
Beginner to intermediate
950 pages
27h 8m
English
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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