Hello, I recently began using R and the lme4 package to carry out linear mixed effects analyses. I am interested in the effects of variables 'prime','time', and 'mood' on 'reaction_time' while taking into account the random effect 'subjects.' I've read through documentation on lme4 and came up with the following formula for use with lmer: reaction_time ~ (mood*prime*soa) + (1|subject) Prime and soa were repeated measures within subjects, while mood was manipulated between subjects. As I understand it, however, this distinction does not affect how the formula should be written. While I've done my background reading and think this formula is correct, I'd appreciate an expert with more experience than I to double check my work. Thanks in advance for any help, Matt The information in this e-mail is intended only for the ...{{dropped:11}}
Matthew Panichello <panichem <at> nmr.mgh.harvard.edu> writes:>[snip]> > I am interested in the effects of variables 'prime','time', and 'mood' > on 'reaction_time' while taking into account the random effect > 'subjects.' I've read through documentation on lme4 and came up with the > following formula for use with lmer: > > reaction_time ~ (mood*prime*soa) + (1|subject) > > Prime and soa were repeated measures within subjects, while mood was > manipulated between subjects. As I understand it, however, this > distinction does not affect how the formula should be written. >[snip] This looks reasonable. If 'prime' and 'soa' vary within individuals, you might want to consider (mood*prime*soa) + (prime*soa|subject): see Schielzeth, Holger, and Wolfgang Forstmeier. 2009. ?Conclusions Beyond Support: Overconfident Estimates in Mixed Models.? Behavioral Ecology 20 (2) (March 1): 416?420. doi:10.1093/beheco/arn145. http://beheco.oxfordjournals.org/content/20/2/416. Followups to r-sig-mixed-models at r-project.org.
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