Calculated values on imputed data - r

Question

I'd like to do something like the following: (myData is a data table)
#create some data
myData = data.table(invisible.covariate=rnorm(50),
visible.covariate=rnorm(50),
category=factor(sample(1:3,50, replace=TRUE)),
treatment=sample(0:1,50, replace=TRUE))
myData[,outcome:=invisible.covariate+visible.covariate+treatment*as.integer(category)]
myData[,invisible.covariate:=NULL]
#process it
myData[treatment == 0,untreated.outcome:=outcome]
myData[treatment == 1,treated.outcome:=outcome]
myPredictors = matrix(0,ncol(myData),ncol(myData))
myPredictors[5,] = c(1,1,0,0,0,0)
myPredictors[6,] = c(1,1,0,0,0,0)
myImp = mice(myData,predictorMatrix=myPredictors)
fit1 = with(myImp, lm(treated.outcome ~ category)) #this works fine
for_each_imputed_dataset(myImp, #THIS IS NOT A REAL FUNCTION but I hope you get the idea
function(imputed_data_table) {
imputed_data_table[,treatment.effect:=treated.outcome-untreated.outcome]
})
fit2 = with(myImp, lm(treatment.effect ~ category))
#I want fit2 to be an object similar to fit1
...
I would like to add a calculated value to each imputed data set, then do statistics using that calculated value. Obviously the structure above is probably not how you'd do it. I'd be happy with any solution, whether it involves preparing the data table somehow before the mice, a step before the "fit =" as sketched above, or some complex function inside the "with" call.

Answer 1

The complete() function will generate the "complete" imputed data set for each of the requested iterations. But note that mice expects to work with data.frames, so it returns data.frames and not data.tables. (Of course you can convert if you like). But here is one way to fit all those models
imp = mice(myData,predictorMatrix=predictors)
fits<-lapply(seq.int(imp$m), function(i) {
lm(I(treated.outcome-untreated.outcome)~category, complete(imp, i))
})
fits
The results will be in a list and you can extract particular lm objects via fits[[1]], fits[[2]], etc