I commend you to (a) the recent article by Doug Bates on "Fitting nonlinear mixed models in R" pp. 27-30 in the latest issue of "R News" available from "www.r-project.org" -> Newsletter and (b) Doug's book with Pinheiro (2000) Mixed-Effects Models in S and S-PLUS (Springer). I suggest you try the same analysis using in "lmer", library(lme4), and "lme", library(nlme), with method = "ML", as explained in Pinheiro and Bates. If you have trouble with this, please post another question on this, preferably using either a standard data set distributed with R or one of the standard packages or a very simple made-up data set with very few observations that you can distribute with your question in a short sequence of R commands illustrating something you tried that either didn't work or that gave results you don't understand. I can't do much more with the example you've provided below, because I don't know how to access the your data. (And PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html if you haven't already.)
hope this helps. spencer graves RenE J.V. Bertin wrote: > Hello, > > I'm trying to understand how to interpret the differences in results between > two versions of a 2-factor ANOVA with (slightly?) different models, of an > observable y, a within-subject factor 'indep' and a grouping factor 'cond' > (and a subject 'factor' Snr): > > >>summary( aov( y~cond + indep + Error(Snr/indep) ) ) > > # example results: > Error: Snr > Df Sum Sq Mean Sq F value Pr(>F) > cond 1 103.1 103.1 1.425 0.248 > indep 5 159.8 32.0 0.442 0.813 > Residuals 18 1301.6 72.3 > > Error: Snr:indep > Df Sum Sq Mean Sq F value Pr(>F) > indep 5 20.81 4.16 3.167 0.0104 * > Residuals 111 145.89 1.31 > --- > Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > Error: Within > Df Sum Sq Mean Sq F value Pr(>F) > Residuals 137 22.178 0.162 > > >>summary( aov( y~cond * indep + Error(Snr/indep) ) ) > > # example results: > Error: Snr > Df Sum Sq Mean Sq F value Pr(>F) > cond 1 174.6 174.6 1.689 0.213 > indep 5 201.9 40.4 0.391 0.848 > cond:indep 5 124.0 24.8 0.240 0.939 > Residuals 15 1550.8 103.4 > > Error: Snr:indep > Df Sum Sq Mean Sq F value Pr(>F) > indep 5 73.16 14.63 8.601 5e-07 *** > cond:indep 5 21.32 4.26 2.507 0.0336 * > Residuals 125 212.64 1.70 > --- > Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > Error: Within > Df Sum Sq Mean Sq F value Pr(>F) > Residuals 464 507.5 1.1 > > > I would like to understand a bit better what the cond:indep line under the > second Error:Snr:indep can mean. If I understood correctly, this represents > some "higher-order" interaction, but not a real indep/cond interaction. What > I also do not grasp is why the indep effect's F and significance is so > different between the two models. > Finally, what does it mean when significant effects are listed under the > Error:Within line? > > Is there a good resource available (web, or if not printed) which discusses > this kind of question in a way accessible to non statisticians? The last time > I checked, manuals like "R for Psychologists" do not really enter into this > level of detail... > > Thanks very much in advance, > R. Bertin > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html