Its not so much that I wasn't getting the difference between fixed and random effects. Although, I do like the way you put the comment below. For my purposes subject is a random effect. It was more on correct notation in lme with repeated measures designs (my a and b are repeated while the mean subjectRT is between). And, on whether the way aov treats repeated measures might best be called a MANOVA method.
On Nov 27, 2003, at 12:54 PM, Spencer Graves wrote:
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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