How to run fixed-effects logit model with clustered standard errors and survey weights in R?

Question

I am using Afrobarometer survey data using 2 rounds of data for 10 countries. My DV is a binary 0-1 variable. I need to use logistic regression, fixed-effects, clustered standard errors (at country), and weighted survey data. A variable for the weights already exists in the dataframe.

I've been looking at help files for the following packages: clogit, glm, pglm, glm2, zelig, bife , etc. Typical errors include: can't add weights, can't do fixed effects, cant do either or etc.

#Glm 

t3c1.fixed <- glm(formula = ethnic ~ elec_prox + 
elec_comp + round + country, data=afb, 
weights = afb$survey_weight, 
index c("country", "round"), 
family=binomial(link='logit'))

#clogit 

t3c1.fixed2 <- clogit(formula = ethnic ~ elec_prox + 
elec_comp + round + country, data=afb, 
weights = afb$survey_weight, 
method=c("within"))


#bife attempt 

library(bife)
t3c1.fixed3 <- bife(ethnic ~ elec_prox + elec_comp + round + 
country, model = logit,data=afb, 
weights = afb$survey_weight, 
bias_corr = "ana")

I either get error messages or the code doesn't include one of the conditions I need to include, so I can't use them. In Stata it appears this process is very simple, but in R it seems rather tedious. Any help would be appreciated!

Answer 1

I would check out the survey package which provides everything for which you are asking. The first step is to create the survey object, specify the survey weights and then you are off to the races.

library(survey)
my_survey <- svydesign(ids= ~1, strata = ~country, wts = ~wts, data = your_data)

# Then you can use the survey glm to do what you want via

svy_fit <- svy_glm(ethnic ~ elec_prox + 
elec_comp + round + country, data = my_survey, family = binomial())

Or at least I would go down this path given you are using survey data.