[R] lme v. aov?

Spencer Graves spencer.graves at pdf.com
Thu Nov 27 17:54:05 CET 2003

      Do you want to make inference about the specific subjects in your 
study?  If yes, the subjects are a fixed effect.  If instead you want to 
make inference about the societal processes that will generate the 
subjects you will get in the future, that is a random effect.  The 
function "lme" handles both fixed and random effects, as does 
"varcomp".  The functions "aov" and "lm" are restricted to fixed effects 
only.  You can use dummy coding for "lm" and "aov" as well. 

      The the distinction between "fixed" and "random" effects seems to 
me to be the same as what Deming called the difference between 
"enumerative" and "analytic" studies:  With a fixed effect / enumerative 
study, the objective is to determine the disposition of the sampling 
frame.  For example, Deming managed a survey of food distribution in 
Japan in 1946 or so, right after World War II.  The purpose was to 
determine where to deliver food the next day, etc., to keep people from 
dying of starvation.  That was an enumerative study.  If the purpose had 
been to advance economic theories for use not only in Japan or in 
1946-47, that is an analytic study. 

      Do you have the book Pinhiero and Bates (2000) Mixed-Effects 
Models in S and S-Plus (Springer)?  If you have more than one use for 
analyzing data on human subjects, I suggest you get and study this book 
if you haven't already.  Doug Bates and several of his graduate students 
have developed "lme".  I am not current in the absolute latest 
literature in that area of statistics, but Bates seems to me to be among 
the leaders in that area and specifically in statistical computing for 
that kind of problem. 

      hope this helps.  spencer graves

John Christie wrote:

> I am trying to understand better an analysis mean RT in various 
> conditions in a within subjects design with the overall mean RT / 
> subject as one of the factors.  LME seems to be the right way to do 
> this. using something like m<- lme(rt~ a *b *subjectRT, random= 
> ~1|subject) and then anova(m,type = "marginal").  My understanding is 
> that lme is an easy interface for dummy coding variables and doing a 
> multiple regression (and that could be wrong).  But, what is aov doing 
> in this instance? MANOVA?  I also haven't been able to find anything 
> really useful on what to properly assign to  "random" in the lme 
> formula.  For repeated measures the use above is always in the examples.
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