From: Eleni Rapsomaniki <e.rapsomaniki_at_mail.cryst.bbk.ac.uk>

Date: Mon 30 Oct 2006 - 08:18:56 GMT

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Mon Oct 30 20:48:26 2006

Date: Mon 30 Oct 2006 - 08:18:56 GMT

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

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Mon Oct 30 20:48:26 2006

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