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I am attempting to run a multilevel model with robust standard errors on multiple imputation output from the MICE package. I can't provide the data, unfortunately, but here is the code I'm running:

imputed <- mice(impvars, maxit = 5)
barg_imp <-complete(imputed, action = "long", include = TRUE)
fitimp <- with(barg_imp,rlmer(success_new ~ conflict_imp + positiondistance_coun +
                      positiondistance_ep + positiondistance_com + population_log + salience_rel 
                      + (1|prnrnmc)))

Note that I'm using rlmer from the robustlmm package. It gives me the error "vector memory exhausted (limit reached?)". I've tried out some of the solutions from other threads on this error message that haven't worked.

I'm able to run this all right when using lmer. Any suggestions on another way to do robust standard errors here (coeftest doesn't work), how to run this code more efficiently or remedying this error? Thank you!

slamballais
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nmariano
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  • You saw this post? https://stackoverflow.com/questions/51295402/r-on-macos-error-vector-memory-exhausted-limit-reached Can you consider testing with fewer rows (eg, sampling 1:1000), or with fewer elements in your formula? – M.Viking Jan 08 '21 at 04:08
  • sorry to be off tangent - but what does this code do? `(1|prnrnmc)` – M.Viking Jan 08 '21 at 04:10
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    (1|prnrnmc) is specifying the level for fixed effects in the mixed effects model. I did see that thread and it hasn't solved my problem. I will try running on a subset just to make sure it actually works. Thanks! – nmariano Jan 08 '21 at 16:47

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