[R] aov documentation page: question

2004-10-25 Thread AJ Smit
Dear all
I was looking at the aov documentation page and came across the 
following which seems like a contradiction to me:

 This provides a wrapper to |lm| for fitting linear models to balanced 
or unbalanced experimental designs. (I presume 'This' refers to aov)

and
|aov| is designed for balanced designs, and the results can be hard to 
interpret without balance

So, do I use it for unbalanced designs?
Thanks,
AJ
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Re: [R] aov documentation page: question

2004-10-25 Thread Prof Brian Ripley
On Mon, 25 Oct 2004, AJ Smit wrote:

 I was looking at the aov documentation page and came across the 
 following which seems like a contradiction to me:

  This provides a wrapper to |lm| for fitting linear models to balanced 
 or unbalanced experimental designs. (I presume 'This' refers to aov)
 
 and

 |aov| is designed for balanced designs, and the results can be hard to 
 interpret without balance

and that is part of a Note, not even a whole sentence of it.  In 
particular, the note has extra force for the multistratum designs 
mentioned there.

 So, do I use it for unbalanced designs?

You _can_ use it, but the results _can_ be hard to interpret.  That is not
a `contradiction' -- perhaps you need to consult your dictionary?  You may
do better to use something else, like lme(), hence the `Note'.

-- 
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, UKFax:  +44 1865 272595

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Re: [R] aov documentation page: question

2004-10-25 Thread Peter Dalgaard
AJ Smit [EMAIL PROTECTED] writes:

 Dear all
 
 I was looking at the aov documentation page and came across the
 following which seems like a contradiction to me:
 
  This provides a wrapper to |lm| for fitting linear models to
 balanced or unbalanced experimental designs. (I presume 'This' refers
 to aov)
 
 and
 
 |aov| is designed for balanced designs, and the results can be hard
 to interpret without balance
 
 So, do I use it for unbalanced designs?

If you know how to interpret the results...

In general, I would avoid it if there is unbalance in the random
effects, but some designs with unbalance in the fixed effects are
manageable. 

-- 
   O__   Peter Dalgaard Blegdamsvej 3  
  c/ /'_ --- Dept. of Biostatistics 2200 Cph. N   
 (*) \(*) -- University of Copenhagen   Denmark  Ph: (+45) 35327918
~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907

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