The problem is that you have 12 observations and 1+2+10=13 degrees of freedom.
There should be 1 + 2 + 8 = 11 degrees of freedom.
Probably one of your variables is masked by something else in you workspace.
Protect yourself by using a data.frame
> tmp <- data.frame(A=factor(c(1,1,1,1,1,1,2,2,2,2,2,2)),
+ B=factor(c(1,1,2,2,3,3,1,1,2,2,3,3)),
+ y=rnorm(12))
> mod <- aov(y ~ A+B, data=tmp)
> summary(mod)
Df Sum Sq Mean Sq F value Pr(>F)
A 1 1.553 1.553 1.334 0.281
B 2 3.158 1.579 1.357 0.311
Residuals 8 9.311 1.164
On Mon, Aug 22, 2016 at 11:15 AM, Justin Thong <justinthon...@gmail.com> wrote:
> Something does not make sense in R. It has to do with the question of
> balance and unbalance.
>
> *A<-factor(c(1,1,1,1,1,1,2,2,2,2,2,2))*
> *B<-factor(c(1,1,2,2,3,3,1,1,2,2,3,3))*
> *y<-rnorm(12)*
> *mod<-aov(y~A+B)*
>
> I was under the impression that the design is balanced ie order does not
> effect the sums of squares. However, when I compute the anova R reports
> that the Estimated Effects are Unbalanced. I thought that when all
> combinations of levels of A and B have equal replications then the design
> is called balanced. But, R tends to think that when not all levels of A and
> levels of B have equal replication, then the "Estimated Effects are
> unbalanced".... Is this the same as the design being unbalanced? Because
> for the example below, where the error occured, the order does not matter
> (which make me think that the design is balanced).
>
>
> *Call:*
> * aov(formula = y ~ A + B)*
>
> *Terms:*
> * A B Residuals*
> *Sum of Squares 0.872572 0.025604 16.805706*
> *Deg. of Freedom 1 2 10*
>
> *Residual standard error: 1.296368*
> *Estimated effects may be unbalanced*
> --
> Yours sincerely,
> Justin
>
> *I check my email at 9AM and 4PM everyday*
> *If you have an EMERGENCY, contact me at +447938674419(UK) or
> +60125056192(Malaysia)*
>
> [[alternative HTML version deleted]]
>
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