- Impute the missing data using Amelia or Mice.
- Estimate the model on each imputed data.
- Use the mitools package to extract and combine results.
For example, here is a simple example:
...
imp <- mice(d)
mydata <- imputationList(lapply(1:5, complete, x = imp))
fit <- lapply(mydata$imputations, function(x){
plm(cog3pl ~ oc + grade9 + boy + han + ruralbirth, data = x,
index = c("schids"), model = "pooling")})
betas <- MIextract(fit, fun = coef)
vars <- MIextract(fit, fun = vcov)
summary(MIcombine(betas, vars))I bet this will work for most, if not all, estimation procedures in R.