Multiple imputation using R

R has a long list of packages for multiple imputation. The main problem is integration: statistical procedures in other packages may or may not work with the imputation procedures. I have been using Amelia together with Zelig. Because they were written by the same group, they work well together. However, I have been having trouble with making multiple imputation to work with the plm package. After searching the internet, here comes the solution:

1. Impute the missing data using Amelia or Mice.
2. Estimate the model on each imputed data.
3. 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.