What is Factorial Design?
Factorial design involves studying the impact of multiple factors simultaneously. Each factor can have multiple levels, and the combinations of these levels form the experimental conditions. This design helps in understanding the main effects of individual factors and their interactions on the response variable.
Factorial designs in R typically rely on several packages that provide specific functionalities:
- stats: Offers foundational tools for data manipulation, statistical modeling (like lm for linear regression), and basic design creation (e.g., expand. grid).
- DoE.base: Provides a comprehensive framework for designing experiments, including full and fractional factorial designs, response surface methodologies, and more advanced experimental designs.
- FrF2: Specifically used for creating regular and non-regular factorial designs, particularly fractional factorial designs of 2k and 3k types.
- DAAG: Offers functions and datasets for design and analysis, focusing on experimental designs for teaching and research.
Factorial Design in R
Factorial designs are powerful tools in experimental design, allowing researchers to efficiently explore the effects of multiple factors and their interactions on a response variable.
In R Programming Language various packages offer capabilities to create, manipulate, and analyze factorial designs. Here, we’ll explore the fundamentals of factorial designs and demonstrate how to implement them using R.