Dear R users, Dealing with mixed models with a binomial DV and interactions between predictors, I still have a few questions I cannot find the answer to. One of my guideline source for the lmer analysis is the Jaeger and Kuperman WOMM slides.
1- all but one predictor are centered, because the latter is a four level predictor and I am interested in contrasts. Is this correct? Thus I cannot interpret the intercept as the grand mean. Does the intercept has any meaning at all? 2- reporting interactions: as a whole and not just specific contrasts For linear models, there is aovlmer.fnc. Is there such a function for mixed models? 3- residualisation In the best model (var1 is centered, var2 is not as it is a factor), var1(2levels) and var2(4levels) have significant interaction and are correlated (-.491, -.527, -.350 for respective contrasts). Residualisation is a possibility. I was advised to use the following code line, but I get an error I cannot fix: corpus$residinteraction = residuals(lm(I(var1*var2) ~ var1 + var2, data= corpus)) The error diagnostic is about having more than two levels for contrast analysis. Thank you very much in advance. Claire Delle Luche Laboratoire Dynamique du Langage 14, avenue Berthelot 69007 Lyon FRANCE _______________________________________________ R-lang mailing list [email protected] http://pidgin.ucsd.edu/mailman/listinfo/r-lang
