[R-sig-ME] Removing random correlation parameter for categorical variable in lmer

[Spätgens, Tessa]

Dear all,

I am still learning how to use mixed effects models and ran into a problem I cannot seem to solve using the advice I can find in articles and on various platforms, so I am hoping someone on the list can help me out!

For a study involving a semantic priming experiment, I want to extract the random slope terms as a measure of semantic priming, using lmer. The related and unrelated items are coded 0 and 1, respectively. I want to extract the individual random slope terms for the unrelated items, as a deviation from the related items (intercept). However, the model shows a perfect correlation between the random intercepts and slopes. I have read that this means that the model is overspecified and that, if removing the random effects altogether is not an option, you can try to remove the correlation parameter itself. However, trying to do this using the formula (0+x|y) still yields a correlation parameter for the subjects with variance 0. The formula looks like this:


Response_time ~ relatedness +  mean_response_time + (0 + relatedness | subject) + (1 | word) + (1 | group)

I think this has to do with the fact that it is a categorical variable, as trying it with a (different) continuous variable does work. Is there any way to remove the correlation parameter in this situation?

Thanks in advance for any advice!

Tessa


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