R help - Oct 2006 - how to combine imputed data-sets from mice for classfication

Dear R users

I want to combine multiply imputed data-sets generated from mice to do
classfication.
However, I have various questions regarding the use of mice library.

For example suppose I want to predict the class in this data.frame:
data(nhanes)
mydf=nhanes
mydf$class="pos"
mydf$class[sample(1:nrow(mydf), size=0.5*nrow(mydf))]="neg"
mydf$class=factor(mydf$class)

First I impute:
imp=mice(mydf)

I want to use randomForest to do my analysis, not the inbuilt glm.mids
functions.
In a previous post it was suggested to substitute the call to (g)lm.mids for the
analysis one needs to perform:
(from http://tolstoy.newcastle.edu.au/R/help/06/03/22295.html)
    analyses <- as.list(1:data$m)
    for (i in 1:data$m) {

        data.i <- complete(data, i)
        analyses[[i]] <- lm(formula, data = data.i, ...)

    }
    
Is the idea that then I should just combine the results(predictions) of
randomForest from all 5 data-sets? In that case what does the pool function do?
Do I need to use it?

Also, if I was to use glm.mids for my predictions I get an
error:
> imp.fit=glm.mids(class ~., data=imp)
Error: NA/NaN/Inf in foreign function call (arg 4)
In addition: Warning messages:
1: - not meaningful for factors in: Ops.factor(y, mu) 
2: - not meaningful for factors in: Ops.factor(eta, offset) 
3: - not meaningful for factors in: Ops.factor(y, mu) 

But this works:
> imp.fit=glm.mids((class=="pos") ~., data=imp)
In this case I don't know how to interpret the result..

I would appreciate any suggestions on these.

Many Thanks
Eleni Rapsomaniki