<br><br><div class="gmail_quote">On Fri, Jul 24, 2009 at 1:34 AM, Claire Delle Luche <span dir="ltr"><<a href="mailto:Claire.Delleluche@univ-lyon2.fr">Claire.Delleluche@univ-lyon2.fr</a>></span> wrote:<br><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Dear R users,<br>
<br>
Dealing with mixed models with a binomial DV and interactions between predictors, I still have a few questions I cannot find the answer to.<br>
One of my guideline source for the lmer analysis is the Jaeger and Kuperman WOMM slides.<br>
<br>
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?</blockquote>
<div><br>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.<br>
<br></div><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
2- reporting interactions: as a whole and not just specific contrasts<br>
For linear models, there is aovlmer.fnc. Is there such a function for mixed models?</blockquote><div><br>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 <i>nested</i> models against a chi-square distribution with df1-df2 degrees of freedom (the difference in number of parameters in the two models).<br>
</div><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;"><br>
<br>
3- residualisation<br>
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).<br>
Residualisation is a possibility.<br>
I was advised to use the following code line, but I get an error I cannot fix:<br>
<br>
corpus$residinteraction = residuals(lm(I(var1*var2) ~ var1 + var2, data= corpus))<br>
<br>
The error diagnostic is about having more than two levels for contrast analysis.</blockquote><div><br>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.<br>
<br>HTH,<br>Florian<br><br> <br></div><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;"><br>
<br>
<br>
Thank you very much in advance.<br>
<br>
Claire Delle Luche<br>
Laboratoire Dynamique du Langage<br>
14, avenue Berthelot<br>
69007 Lyon FRANCE<br>
<br>
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</blockquote></div><br>