# Solved – Fixed effects have df=0 and chi-sq=0 when tested using anova()

I have a made a linear mixed model using `lmer()`.

I began with a maximal model containing 6 terms and all two-way interactions (15 total), I also have a random effect of subject ID. Of these terms, 11 interactions were significant and so were kept in the final model. I found this by dropping interactions and comparing to previous model using `anova()`. As all 6 fixed terms are part of a significant interaction they remained in the model. My problem is that when I tried to test the fixed terms to find a chi-squared value for them by removing the term and comparing to the full model, the `anova` output said `chisq = 0, df=0` but that `p< 2.2e-16`. I don't understand how p can be so small if `chisq = 0`. I also don't understand why `df=0` which would suggest I am comparing the same model when actually I have removed a term. What does this mean? Have I fit too many interactions to my data (I only have 160 data points). Is there any point in finding the chi-squared value for these fixed effects?

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Probably because you write the wrong code. I got `df=0` too, and found out why.
For example: I used the formula: `y ~ x1 + x2 +x1*x2 + (1|x3)` in `lmer`, and then: `y ~ x2 +x1*x2 + (1|x3)`.
Then I used `anova` to get the p-value for `x1` using the above two `lmer` outputs. It gives result `df=0`, `chisq=0`, and very small p.
I found out that I misunderstood `*`; `x1*x2` means `x1 + x2 + x1:x2`, whereas `x1:x2` is the interaction of `x1` and `x2`.