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Dear R-Langs,<br>
<br>
I'm currently trying to analyze some experimental data with a contrast
coded mixed model. I have a 2 (levels:A,B) x 3 (levels:D,E,F) design
with unbalanced cell sizes. I am coding the following variables:<br>
<br>
AvB, DvE, EvF<br>
<br>
where sum(AvB)=0, sum(DvE)=0, sum(EvF)=0. Next I fit this model:<br>
<br>
lmer(DV~AvB*(DvE+EvF)+(1|Subj)+(1|Item))<br>
<br>
And here is the correlation matrix it outputs:<br>
<br>
Correlation of Fixed Effects:<br>
(Intr) AvB DvE EvF AvB:DvE<br>
AvB -0.029 <br>
DvE 0.010 -0.006 <br>
EvF -0.002 -0.001 <b><font color="#cc0000">0.488</font> </b>
<br>
AvB:DvE -0.003 0.015 -0.039 -0.018 <br>
AvB:EvF -0.001 -0.002 -0.018 -0.044 <font color="#000099"><b>0.488 </b></font><br>
<br>
The question I have is, how to get rid of the collinearity in red and
blue and whether it's even possible. And if it's not possible, in what
way will this affect the reliability of my result?<br>
<br>
Thanks so much in advance,<br>
<br>
Peter<br>
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