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I am trying to predict a binary outcome with a model that includes a random effect using survey data. I've included a description of the sampling design below, so feel free to comment on my survey weighting approach. My primary question is how to include a random effect in the survey weighted 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 the random effect to be on the region. Thanks,

Sampling Description:

The MICS 2006 used a two-stage stratified sample design. At the first stage of sampling, 300 census enumeration areas (124 urban and 176 rural EAs) were selected. These are a subsample of the 660 EAs (281 urban and 379 rural) selected for the GLSS 5. The clusters in each region were selected using systematic sampling with probability proportional to their size.

mike
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1 Answers1

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This isn't available in the survey package and I'm fairly sure it isn't available in R.

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

Design-based inference for mixed models is an area of current research.

Thomas Lumley
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