R help - Feb 2006 - Pairwise comparison after repeated measures ANOVA

I am analyzing some data obtained after measuring some parameters at
different times in samples obtained from many subjects. The model is
quite simple: aov(parameter ~ Time + Error(Subject/Time))

Now I want to make a pairwise comparison between the levels of Time.
However, I have not find how to do such a thing. I cannot use TukeyHSD
or pairwise.t.test, I supposse. Maybe using contrasts?

Could you aim me at some kind of information? All I have found is
information on ANOVA, but not how to compare the levels of the factors
afterwards.

Maybe I simply do not understand how this really works, my background is
not very deep in statistics.
Thank you.

Felipe

--------------------oOo--------------------
Felipe Mart??nez Pastor, Ph. D.
Ciencia y Tecnolog??a Agroforestal
ETSIA-UCLM
Av. Espa??a s/n
02071-Albacete (Spain)
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e-mail: Felipe.Martinez at uclm.es
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Felipe Mart伱伃nez-Pastor <Felipe.Martinez <at> uclm.es> writes:
> 
> I am analyzing some data obtained after measuring some parameters at
> different times in samples obtained from many subjects. The model is
> quite simple: aov(parameter ~ Time + Error(Subject/Time))
> 
> Now I want to make a pairwise comparison between the levels of Time.
> However, I have not find how to do such a thing. I cannot use TukeyHSD
> or pairwise.t.test, I supposse. Maybe using contrasts?
> 
I would suggest that you use lme from package nlme for this type of a problem, 
because it provides a MUCH better toolbox fro what noadays is called mixed-
effect models. And get the book by Pinheiro/Bates (Mixed-Effect Models in S and 
S-PLUS), which is full of practical examples of this type.

With the results of <lme>, you can use <estimable> from package
gmodels to
estimate contrasts, and p.adjust to adjust for multiple comparison. However, as 
you parameter is called Time, you should really rethink if this is the right 
way to go. Why not treat Time as and continuous variable, effectively 
estimating a slope? Or at least as an ordered variable? I know from my clients 
that, after measuring a parameter 6 instances in time, they insist on a star-
spangled list of all 15 pairwise comparisons. If they do, I take out  
Bonferroni's Iron Claw even if I know better methods.

The question: "At what time intervals is the difference significant"
is mostly
nonsense.

Try to rephrase as "Is there a significant linear trend in time?".
Would adding
a quadratic component improve the quality of the fit (use AIC for this, see 
Pinheiro/Bates or Venables/Ripley). In many biological applications I know, a 
linear trend is perfectly fine, and it often has an easy interpretation. You 
only have to fight "but it's not linear" faculty, who mostly would
not object
to computing the mean without ever thinking to ask "but it's not
constant".

Soapbox off. You see, I just had a discussion on the subject with a client.

Dieter

Srinivas Iyyer wrote:
> Hi Group,
>  I have two objects
> 1. A data.frame with 2 columns
> 
> Apple  4
> Boy    2
> Cat    5
> Dog    10
> Eel    9
> ...
> Zebra  10
> (26 rows)
> 
> I have another object which was obtained as vector of
> integers with names as character.
> 
> Fruit 10
> King 20
> Eel  19
> Boy 20
> Apple 15
> ...
> (26 rows)
> 
> Now I wanted to combine the two objects and wanted a
> result that would look like this:
> 
> Apple  4 15
> Boy    2 20
> Cat    5 28
> Dog    10 25
> Eel    9 19
> 
> 
> result <- cbind(obj1, obj2)
> 
> My result is now jumbled:
> 
> Apple  4 34
> Boy    2 21
> Cat    5 234
> Dog    10 23
> Eel    9  12
> 
> I want to write identical row names elements together.
> 
> 
> What could be done here. Could any one please help me.
> 

See ?merge

Uwe Ligges

> thanks
> Srini
> 
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