I understand now
On May 30, 4:04 pm, Bogdan Lataianu <bodins...@gmail.com> wrote:
> Read the data using scan():
> #
> # a1 a2 a3 a4
> # ------------- ------------- ------------- -------------
> # b1 b2 b3 b1 b2 b3 b1 b2 b3 b1 b2 b3
> # --- --- --- --- --- --- --- --- --- --- --- ---
> #
> # c1:
> # 4.1 4.6 3.7 4.9 5.2 4.7 5.0 6.1 5.5 3.9 4.4 3.7
> # 4.3 4.9 3.9 4.6 5.6 4.7 5.4 6.2 5.9 3.3 4.3 3.9
> # 4.5 4.2 4.1 5.3 5.8 5.0 5.7 6.5 5.6 3.4 4.7 4.0
> # 3.8 4.5 4.5 5.0 5.4 4.5 5.3 5.7 5.0 3.7 4.1 4.4
> # 4.3 4.8 3.9 4.6 5.5 4.7 5.4 6.1 5.9 3.3 4.2 3.9
> #
> # c2:
> # 4.8 5.6 5.0 4.9 5.9 5.0 6.0 6.0 6.1 4.1 4.9
> 4.3
> # 4.5 5.8 5.2 5.5 5.3 5.4 5.7 6.3 5.3 3.9 4.7 4.1
> # 5.0 5.4 4.6 5.5 5.5 4.7 5.5 5.7 5.5 4.3 4.9 3.8
> # 4.6 6.1 4.9 5.3 5.7 5.1 5.7 5.9 5.8 4.0 5.3 4.7
> # 5.0 5.4 4.7 5.5 5.5 4.9 5.5 5.7 5.6 4.3 4.3 3.8
> #
> # NOTE: Cut and paste the numbers without the leading # or labels
> #
>
> > Y <- scan()
> > A <- gl(4,3, 4*3*2*5, labels=c("a1","a2","a3","a4"));
> > B <- gl(3,1, 4*3*2*5, labels=c("b1","b2","b3"));
> > C <- gl(2,60, 4*3*2*5, labels=c("c1","c2"));
> > anova(lm(Y~A*B*C)) # all effects and interactions
>
> In the above example, why the number of replications for A is 3, for B
> is 1 and for C is 60?
> And why 4*3*2*5? Is the 5 because there are 5 lines in each 4*3*2
> group?
> What is the logic behind this?
>
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