Weighted Random Effects Weighting Model in R

Question

I am trying to predict a binary result with a model that includes a random effect using survey data. I have included a description of the sample design below, so feel free to comment on my approach to weighting the survey. My main question is how to incorporate a random effect into a balanced research model. Here is the code up to this point:

# Libraries library(survey) # Make dataframe object where d is the working dataframe dfobj <- svydesign((id = ~cluster+household, strata = ~interaction(region, urban) weights = ~chweight, strata = ~strata, data = d) # Run a logit model formula1 <- stunting ~ modern_toilet + diarrhoea + fever + insurance + sex + age + region_code model1 <- svyglm(formula=formula1,design=dfobj,family = quasibinomial) 

I would like a random effect to be in the area. Thanks,

Sampling Description:

MICS 2006 used a two-stage stratified sample. In the first stage of sampling, 300 census regions were selected (124 city and 176 rural experts). This is a subset of 660 EAs (281 urban and 379 rural) selected for GLSS 5. The clusters in each were selected using a systematic sample with a probability proportional to their size.

Answer 1

This is not available in the survey package, and I'm sure it is not available in R.

There are methods for a generalized linear mixed model with survey data when clusters for random effects are the same as sampling units. There are implementations in Stata (-gllamm-) and MLwin, and possibly others.

The constructive conclusion for mixed models is the area of ​​current research.