Looping to extract coefficients from multiply imputed mer objects

I am having a hard time wrapping my head around this problem. I have a list, results4 which contains 5 elements, all of which are mer objects from the zelig package. The mer objects are the result of ls.mixed regressions on each of five imputed datasets. I am trying to combine the results using Rubin's Rules for Multiple Imputation.

I can extract the coefficients and standard 开发者_运维百科errors using summary(results4[[1]])@coefs, which returns a 16x3 vector (16 variables, each with a point estimate, standard error, and t-statistic).

I am trying to loop over the five sets of results and automate the process of combining the point estimates and standard errors, but unfortunately I seem to be staring at it with no solution arising. Any suggestions?

The code that produces the mer objects follows (variable names changed):

for (i in 1:5) {
  results4[i] <- zelig(DV ~ V1 + V2 + V3 + V4 + V5 + V6 + V7 + V8 +
  V9 + V10 + V11 + V12 + V13 + V14 + V15 + tag(1 | L2),
  data = as.data.frame(w4[,,i]), model = "ls.mixed", REML = FALSE) 
}

I'm not going to take the time to code up the multiple-imputation rules (someone who wants the credit can what I show here and build on it), but I think you should be able to do what you want by building a 16x3x5 array containing the results:

resultsList <- lapply(results,function(x) summary(x)@coefs)
library(abind)
resultsArr <- abind(resultsList,along=3)

and then using apply appropriately across the margins.

There's probably a plyr-based solution as well.

You could also do this less fancily by just defining the array up front and filling it in as you go:

sumresults <- array(dim=c(16,3,5))
for (...) {
   ...
   sumresults[,,i] <- summary(results4[[i]])@coefs
}