Hi Mark,
Thanks for your response. After some detective work I figured out the answer
to my question.
The models
> lm.split=lm(Y~B*V+B*N+V*N)
> lmer.split=lmer(Y~V+N+V:N+(1|B)+(1|B:V)+(1|B:N))
contain exactly the same terms. The difference is that blocking factor (B)
is fixed in first model b
Hi:
Look at the structure of the experiment.
The six blocks represent different replications of the experiment.
No treatment is assigned at the block level.
Within a particular block, there are three plots, to which each
variety is randomly assigned to one of them. Ideally, separate
randomization
Like James Booth, I find the SSQ and MSQ in lmer output confusing. The
F-ratio (1.485) for Variety is the same for aov, lme and lmer, but
lmer's mean square for variety is 1.485 times the subplot residual mean
square. In the conventional anova table for a split-plot expt, the
variety mean square is
Hi Jim,
>> The decomposition of the sum of squares should be the same regardless of
>> whether block is treated as random of fixed.
Should it? By whose reckoning? The models you are comparing are different.
Simple consideration of the terms listed in the (standard) ANOVA output
shows that this
The following output results from fitting models using lmer and lm to
data arising from a split-plot experiment (#320 from "Small Data Sets"
by Hand et al. 1994). The data is given at the bottom of this message.
My question is why is the sum of squares for variety (V) different in
the ANOVA t
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