"William Simpson" <[EMAIL PROTECTED]> writes:
> > Your lme statement is OK. To get the usual split-plot anova, your aov
> > statement should be
> >
> > fit2 <- aov(y ~ a*b*c + Error(s), data = d)
>
> No, this gives wrong F-values. By "wrong" I mean it does not agree with the
> published table.
Well, it's the model that is equivalent to your lme() model....
Thing is that you want to add random effects of s:b and s:c, which are
crossed factors, so somewhat tricky to code with lme() (this sort of
thing is easier in lmer() from the lme4 packages).
The generic way to handle this in lme() is via something like
random=list(s=pdBlocked(list(
pdIdent(~1),
pdIdent(~b-1),
pdIdent(~c-1))))
You probably won't get the degrees of freedom right, though.
> Table 12.10-2, page 559:
> Number of obs = 32 R-squared = 0.9920
> Root MSE = .559017 Adj R-squared = 0.9589
>
> Source | Partial SS df MS F Prob > F
> -----------+----------------------------------------------------
> Model | 233.625 25 9.345 29.90 0.0002
> |
> a | 3.125 1 3.125 2.00 0.2070
> s|a | 9.375 6 1.5625
> -----------+----------------------------------------------------
> b | 162.00 1 162.00 199.38 0.0000
> a*b | 6.125 1 6.125 7.54 0.0335
> b*s|a | 4.875 6 .8125
> -----------+----------------------------------------------------
> c | 24.50 1 24.50 61.89 0.0002
> a*c | 10.125 1 10.125 25.58 0.0023
> c*s|a | 2.375 6 .395833333
> -----------+----------------------------------------------------
> b*c | 8.00 1 8.00 25.60 0.0023
> a*b*c | 3.125 1 3.125 10.00 0.0195
> |
> Residual | 1.875 6 .3125
> -----------+----------------------------------------------------
> Total | 235.50 31 7.59677419
>
> Bill
>
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>
--
O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B
c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907
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