CODEWITHSUNDEEP
rregressionlme4
Svenja
Svenja

Reputation: 21

lmer gives estimates for non-existing variables after equation with interaction terms

I have a dataset on students in classrooms. After multiple multilevel imputation with mice (pooling with mitml), I have 20 datasets. I now want to apply multilevel regression. The "normal" regressions work fine, but as soon as I include interaction terms I don't understand the output anymore. Example:

I want to calculate the effect of the interaction between mean achievement (meanmath, L2) and classroom climate (cc, L2) on individual achievement (math, L1). The equation looks like this:

Int1 <- with(data, lmer(math ~ meanmath*cc + (1|classID)))

In the output I now get the following estimates:

(Intercept) 0.34
meanmath    0.22
cc1        -0.43
cc2        -0.69
cc3        -0.66
meanmath*cc1 -0.16
meanmath*cc2 0.12 
meanmath*cc3 0.23

These cc1-3 variables do not exist in my dataset, neither in the original one, nor in the imputed ones. Could maybe someone tell me how I could find where these variables come from?

I tried to run the equation with just one of the imputed datasets -> Same thing happened I made sure that there are the same variables in all imputed datasets -> This is the case

Details from comment:

data$cc is a Factor w/ 4 Levels: "1", "2", "3", "4". Otherwise the variables are all continuous.

Upvotes: 0

Views: 35

Answers (1)

Svenja
Svenja

Reputation: 21

Thanks to the very helpful comments I now understood that this is not an R problem, but rather a statistical one. If the interaction term includes a factor, the estimates show the effects of factor levels in comparison to a reference level. In the estimates I posted above, cc4 is the reference level. This means that e.g. students in classrooms with climate 3 (cc3) have an expected value of -0.66 compared to students in a classroom with climate 4 (cc4). Again a big thank you to all the contributors of the comments that helped me to understand this!

Upvotes: 2

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