interpreting intercept in lme4 when having within and between subjects variables and interaction I am testing the effect of priority (which is a continuous within subjects factor) and viewing condition (which is a factor and a between subjects factor with 2 levels) on error. So the condition factor was coded with 0 being the free viewing condition and 1 being the fixed viewing condition. I am also investigating the interaction between priority and viewing condition. I used the following code:
Mag2 = lmer(Error ~ Priority*Condition + (1|Subject), data = Magnitude, REML = FALSE)
anova(Mag2)

And I get the following output about fixed effects:
Fixed Effects:
            (Intercept)                 Priority           Conditionfixed   
                75.9227                  -0.4155                  -9.4225                    

Priority:Conditionfixed 0.2342  

I know that since condition is a factor with two levels and I coded it in the above way, the (Intercept) refers to free condition while Conditionfixed refers to fixed condition. So I know by looking at the averages that I have more error in the fixed condition than in the free so i would expect the Conditionfixed value to be positive. But here is negative? Why is that? Am I interpreting it in a wrong way?
Thank you

Which averages are you comparing, exactly? More specifically, with which Reputation values?
Assuming free viewing is the reference condition, I think the answer may be in the interaction term: for each increment of Priority with 1 unit, there is a decrement of -0.4155 for the free viewing condition, while the decrement is limited to -0.4155 + 0.2342 = -0.1813 for the fixed viewing condition. So provided Reputation scores are sufficiently high, this difference could more than compensate the between condition intercept.

