[R-sig-ME] Using Robust Standard Errors lme4

Fernando Pedro Bruna Quintas |@brun@ @end|ng |rom udc@e@
Mon Nov 8 14:51:34 CET 2021


I have tried to do that with lme4 and the function tab_model() of pacakge sjPlot. That works for other types of models, but it did not worked for lme4. Try WeMix package, which mimic Stata�s mixed function.

However, I have substantive doubts about this. Do  robust standard errors make sense in multilevel models?. For instance, using clustered standard errors is a way of introducing level-two correlations in OLS, but in hierarchical models we are already modelling that correlation. Additionally, there are more general doubts about that. See:

I would like to listen more thoughts about robust and clustered standard errors in multilevel models, both from the philosophical point of view and the practical one.


Fernando Bruna
Department of Economics
Universidade da Corunha, Spain.

De: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> en nombre de Suresh N Neupane via R-sig-mixed-models <r-sig-mixed-models using r-project.org>
Enviado: lunes, 8 de noviembre de 2021 12:49
Para: r-sig-mixed-models using r-project.org <r-sig-mixed-models using r-project.org>
Asunto: [R-sig-ME] Using Robust Standard Errors lme4

Dear all,
I am using lme4 package to run a hierarchical logistic regression model (with random county effect) for my binomial dependent variable, COVID death (0/1). This is a very large dataset with ~ 8 million observations.
I need to find the robust standard error but was not sure about any package. I tried robustlmm (although my DV is not continuous) and got this error when used MerDeriv (sandwich):
Error in UseMethod("estfun") :
no applicable method for 'estfun' applied to an object of class "c('glmerMod', 'merMod')"
The real tricky part is it takes several hours to compute because of the dataset length.
All I need is to get robust standard error values.
I already fit the models:
fit <- glmer(death ~ (1|county_fips_code) + IV).
Thank you so much,
Suresh Neupane

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