Using Robust Standard Errors lme4

Hi Daniel,
Thank you so much for your suggestions. I tried that but it gives me an
error:

Error in UseMethod("estfun") :
  no applicable method for 'estfun' applied to an object of class
"c('glmerMod', 'merMod')"

My DV is death (0/1) and IV other variables:
Model: (with random county effects):
fit <- glmer(death ~ (1|county_fips_code) +white + black + pop.density ,
family = binomial("logit"), nAGQ=0, data = data.thesis)
 I tried the code you sent and used "county_fips_code" as my cluster
variable but it says did not find "county_fips_code".
I was able to use parameters::model_parameters(fit) to extract some info
(This is great!) such as;
 Parameter     |  Log-Odds |   SE |  95% CI |   z |     p

Thank you so much again,
I'd really appreciate if you would have more input on this.
Sincerely,
Suresh




------------------------------
*From:* Daniel L?decke <d.luedecke at uke.de>
*Sent:* Tuesday, November 9, 2021 4:32 AM
*To:* Suresh N Neupane <sneupane2 at student.gsu.edu>
*Cc:* r-sig-mixed-models at r-project.org <r-sig-mixed-models at r-project.org>
*Subject:* AW: [R-sig-ME] Using Robust Standard Errors lme4

Hi Suresh,

you could try the "parameters" package, which should (easily) return
different types of robust standard errors, including cluster-robust
standard
errors for mixed models:


https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Feasystats.github.io%2Fparameters%2Farticles%2Fmodel_parameters_robust.html&amp;data=04%7C01%7Csneupane2%40student.gsu.edu%7Ca99f7d4a171f419a724a08d9a363ee14%7C704d822c358a47849a1649e20b75f941%7C0%7C0%7C637720471875578865%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&amp;sdata=DOoXhh%2FzuFnNc6ZTAb9hry9%2FSB7QD%2F7bEYX%2BaHLQBhE%3D&amp;reserved=0

Maybe you try this out on a very small subset of your model to see if it
works. Also, since the "model_parameters()" function includes random
effects
variances by default, you may set the effects-argument to "fixed" to speed
up computation. The call to the function could then look like something
like
this:

model_parameters(
  model,
  effects = "fixed",
  robust = TRUE,
  vcov_estimation = "CL",
  vcov_type = "HC1",
  vcov_args = list(cluster = <your cluster variable from the data>)
)


Best
Daniel

-----Urspr?ngliche Nachricht-----
Von: R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> Im
Auftrag von Suresh N Neupane via R-sig-mixed-models
Gesendet: Montag, 8. November 2021 12:49
An: r-sig-mixed-models at r-project.org
Betreff: [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