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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#### Best Answer

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`

.