I'm using ANOVA to test for differences between different values of the same factor for a mixed effects model which I produced. My model is:
m2 <- lmer (ovsize ~ dm2 <- lmer (ovsize ~ d.sheetratio + (1|nid), REML=FALSE).sheetratio + (1|nid), REML=FALSE)
Following Following this, I subsetted the data so that each separate model described just data values for just one value of ratio.
me <- lmer (eovsize ~ eratio + (1|nide), REML=FALSE)
md <- lmer (dovsize ~ dratio + (1|nidd), REML=FALSE)
mc <- lmer (covsize ~ cratio + (1|nidc), REML=FALSE)
mb <- lmer (bovsize ~ bratio + (1|nidb), REML=FALSE)
ma <- lmer (aovsize ~ aratio + (1|nida), REML=FALSE)
me <- lmer (eovsize ~ eratio + (1|nide), REML=FALSE)
md <- lmer (dovsize ~ dratio + (1|nidd), REML=FALSE)
mc <- lmer (covsize ~ cratio + (1|nidc), REML=FALSE)
mb <- lmer (bovsize ~ bratio + (1|nidb), REML=FALSE)
ma <- lmer (aovsize ~ aratio + (1|nida), REML=FALSE)
At this point I get an error message which states that 'fixed-effect model matrix is rank deficient so dropping 1 column / coefficient'.
Then
Then when I do the ANOVA to compare each (e.g. meme vs ma. ma) I get an output which looks like this:
Data:
Models:
mb: bovsize ~ bratio + (1 | nidb)
ma: aovsize ~ aratio + (1 | nida)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
mb 3 -323.05 -316.06 164.53 -329.05
ma 3 -235.34 -229.37 120.67 -241.34 0 0 1
Data:
Models:
mb: bovsize ~ bratio + (1 | nidb)
ma: aovsize ~ aratio + (1 | nida)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
mb 3 -323.05 -316.06 164.53 -329.05
ma 3 -235.34 -229.37 120.67 -241.34 0 0 1
How can $\chi^{2}$ be 1 when each model is different? Is there an error in the method that I've used? Also, is the error message which I received important?
