Mike Lawrence <mike <at> thatmike.com> writes: > > And if I decided to ignore the "type" variable altogether and simply > use the continuous "valence" variable, this is what I'd use? > > summary(lme( > fixed = rt~valence*color > , data = a > ,random = ~1|id > )) > > I also have continuous luminance measurements of each color that vary > from participant to participant (we used different monitors). If I > were interested in luminance *instead* of color, would the following > be appropriate, or do I need to do something special to account for > the fact that each participant has different values of luminance? > > summary(lme( > fixed = rt~valence*luminance > , data = a > ,random = ~1|id > ))
Both might be appropriate, but be sure to understand the implications. Both valence and luminance now are to be interpreted as slopes. Since slope-interactions are a bit awkward to interpret, I would prefer to start with fixed = rt~valence+luminance fixed = rt~valence*luminance-valence:luminance Both mean the same, the latter is ridiculous here, but may be useful when you have more terms to remove higher ones. Also look at the meaning of ^2, and the difference of I()^2. You might have a look at stepwise procedures in stepAIC, even if in my field there are good reasons to avoid this type of model selection. It can be tricky to explain slope interactions in papers, but it's probably easier in psychology where people are ready to accept models than in medicine, were everything beyond a t-test is frowned upon by reviewers. Dieter ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.