Factorial treatment designs are popular, due to advantages of research on multiple treatment factors and how they interact. But if the design includes a control treatment that is not part of the factorial, problems occur in estimation of least squares means. A typical example is shown here, with 2 fertilizer and 3 irrigation treatments, giving 6 factorial treatment combinations, plus a control that is defined by a 3rd level of fertilizer, and a 4th level of irrigation: Fert1:Irrig2 Fert2:Irrig1 Fert1:Irrig1 Fert2:Irrig3 Fert1:Irrig3 Fert2:Irrig2 Control Other situations might have the control sharing a level of one of the factors, for example the control might be defined as Fert2:Irrig4. But this still causes problems with estim...
Blogging on use of SAS and R software to perform statistical tasks for research data...