lme() direction
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 )) On Sat, Feb 7, 2009 at 12:34 PM, Dieter Menne
<dieter.menne at menne-biomed.de> wrote:
Mike Lawrence <mike <at> thatmike.com> writes:
Would it improve things if "type" were a continuous variable rather than categorical? I chose words at the extreme ends of a valence rating scale but I still have the raw valence ratings for each word.
With the interaction, the extreme would be summary(lme(rt~type*color*word, data=a,random=~1|id)) or, less extreme summary(lme(rt~type*color+color:word, data=a,random=~1|id))
.. Something like summary(lme(rt~type*color+color:as.numeric(word), data=a,random=~1|id)) (please replace as.numeric() by the raw valence, the example above it simply wrong) could gain you a few degrees of freedom if you are willing to accept the linear hypothesis. And as there is something like raw valence, one should not throw away details about a-priori ordering in favor of a categorical hypothesis. Dieter
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Mike Lawrence Graduate Student Department of Psychology Dalhousie University www.thatmike.com Looking to arrange a meeting? Check my public calendar: http://www.thatmike.com/mikes-public-calendar ~ Certainty is folly... I think. ~