On Fri, Jul 24, 2009 at 1:34 AM, Claire Delle Luche < [email protected]> wrote:
> 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? The intercept always has the meaning of "everything else is 0" --> when the sum of all other beta * predictors is 0 (e.g. when all other predictors are 0), then the linear predictor is the intercept. So, if you have a balanced sample and the all predictors are contrast coded except for one 4 level predictor, which is treatment coded, then the intercept corresponds to the mean of the reference condition of the 4-level predictor. 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? aovlmer.fnc is for lmer (=mixed) linear models. Do you mean mixed logit models? You can always do the same thing yourself by comparing the deviance of *nested* models against a chi-square distribution with df1-df2 degrees of freedom (the difference in number of parameters in the two 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. in order to make var1*var2 a continuous outcome (expected by lm()) you need to manually recode the factors in to k-1 numerical predictors where k is the number of levels in the predictor. I suspect that your error message is linked to this problem. HTH, Florian > > > > 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 >
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