Thank you for the suggestions from Professor Ripley and Steve Elliot. I see now
why my data are unbalanced even though I don't have any missing data.
I think I should use other methods designed for unbalanced data, but does using
lme with plate as a random effect also help to fix this problem? I am still
very new at this type of analysis.
Thank you for the help.
Brooke
-----Original Message-----
> Date: Tue Sep 18 12:38:37 EDT 2007
> From: "Prof Brian Ripley" <[EMAIL PROTECTED]>
> Subject: Re: [R] unbalanced effects in aov
> To:
>
> On Fri, 14 Sep 2007, Brooke LaFlamme wrote:
>
> > Hi, I have been having some trouble using aov to do an anova, probably
> > because I'm not understanding how to use this function correctly. For
> > some reason it always tells me that "Estimated effects may be
> > unbalanced", though I'm not sure what this means. Is the formula I am
> > using written incorrectly? Below is the code I am using along with the
> > data:
>
> [...]
>
> > I am treating all the variables as factors (except for response, obviously).
> >
> > formula<-response~species+line%in%species+replicate%in%line+sex%in%species+plate
> > model<-aov(formula, data=my.data)
> >
> > This is the output:
> >
> >> model
> > Call:
> > aov(formula = formula, data = my.data)
> >
> > Terms:
> > species plate species:line line:replicate
> > Sum of Squares 0.0026469288 0.0000945202 0.0003320255 0.0002008000
> > Deg. of Freedom 2 11 27 10
> > species:sex Residuals
> > Sum of Squares 0.0001383116 0.0006315465
> > Deg. of Freedom 3 66
> >
> > Residual standard error: 0.003093362
> > Estimated effects may be unbalanced
> >
> > Any help would be greatly appreciated as the R help documentation for
> > aov does not address this issue.
>
> For the benefit of those who are unable to appreciate
> fortunes::fortune("WTFM"), the help page actually says
>
> 'aov' is designed for balanced designs, and the results can be
> hard to interpret without balance: beware that missing values in
> the response(s) will likely lose the balance. If there are two or
> more error strata, the methods used are statistically inefficient
> without balance, and it may be better to use 'lme'.
>
> Balance can be checked with the 'replications' function.
>
> So let's do as it suggests:
>
> > replications(formula, data=my.data)
> $species
> [1] 40
>
> $plate
> plate
> 1 2 3 4 5 6 7 8 9 10 11 12
> 11 11 11 10 9 11 10 11 10 11 9 6
>
> $`species:line`
> [1] 4
>
> $`line:replicate`
> [1] 6
>
> $`species:sex`
> [1] 20
>
> and the problem will be clear to those who have read ?replications.
>
> --
> Brian D. Ripley, [EMAIL PROTECTED]
> Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
> University of Oxford, Tel: +44 1865 272861 (self)
> 1 South Parks Road, +44 1865 272866 (PA)
> Oxford OX1 3TG, UK Fax: +44 1865 272595
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