Fitting multinomial and generalized ordered logistic models with `survey` and `svyVGAM`

survey-multinomial-and-generalized-ordered-logit-models.r

library(survey)
library(svyVGAM)
data(api)

# Prepare data with binary, categorical, and ordered outcomes ----

  apiclus2 <- transform(apiclus2, mealcat = cut(meals,c(0,25,50,75,100)))
  apiclus2 <- transform(apiclus2, mealcat_ordered = as.ordered(cut(meals,c(0,25,50,75,100))))
  apiclus2 <- transform(apiclus2, mealcat_high = as.ordered(cut(meals, c(0,50,100))))
  
# Set up the survey design object ----
dclus2 <- svydesign(id = ~dnum+snum, fpc = ~fpc1+fpc2, data = apiclus2)

# Fit a binary logistic model ----
  
  bin_model <- svyglm(formula = mealcat_high ~ avg.ed + mobility + stype,
                      family = quasibinomial(),
                      design = dclus2)
  
# Fit a multinomial model ----
  mn_model <- svy_vglm(formula = mealcat~avg.ed+mobility+stype,
                       family = multinomial(refLevel = 1),
                       design = dclus2)
  
# Fit a generalized ordered logistic model ----
  
  geologit_model <- svy_vglm(formula = mealcat_ordered ~ avg.ed + mobility + stype,
                             family = cumulative(link = "logitlink", parallel = FALSE),
                             design = dclus2)