Stats beginner here. I have a dataset composed of observations taken from 16 separate experimental panels, each nested into one of 4 conditions (Treatment A Level 1, Treatment A Level 2, Treatment B Level 1, Treatment B Level 2; see photo: http://imgur.com/ZbzFPNq). There are 100 observations of the dependent variable for each of the 16 panels (1600 total obs). I'm trying to determine main effects of both treatment types, and any interaction effect, accounting for the within-panel variation. I'm trying to determine if the DV differs significantly across conditions. Is the appropriate model a mixed model with panel-groups as a random factor? Eg: glmer(DV~TreatmentA*TreatmentB + (1|panel.group)) how should I be constructing the code? or... is it more appropriate to treat panels as a fixed factor? Thanks! KC [[alternative HTML version deleted]]
K C <interlocutorbrl2 <at> gmail.com> writes:>[snip]> I have a dataset composed of observations taken from 16 separate > experimental panels, each nested into one of 4 conditions (Treatment A > Level 1, Treatment A Level 2, Treatment B Level 1, Treatment > B Level 2; see > photo: http://imgur.com/ZbzFPNq). There are 100 observations of the > dependent variable for each of the 16 panels (1600 total obs). > > I'm trying to determine main effects of both treatment types, and any > interaction effect, accounting for the within-panel variation. I'm trying > to determine if the DV differs significantly across conditions.This is a stats question, not an R question per se ... I would recommend posting to CrossValidated (http://stats.stackexchange.com) good luck, Ben Bolker