Re: st: RE: impute: too many variables specified

[Chris Wallace <c.wallace@qmul.ac.uk>]

Nick Cox wrote:

> This was asked a while back. I can't remember 
> the answer but it should be in the archives.
Thanks (and to Richard Williams and Kit Baum) - I found the mail and now 
understand the problem.


> A quite different comment is to ask how much 
> better your imputation is based on 30+ predictors
> rather than 30. 
>  
The situation is that I have 17 biochemical variables, each measured 
twice (call them A1, A2, ..., Q1, Q2). Each of these need to be 
transformed into Xsum=X1+X2 and Xdiff=abs(X1-X2) - these will then be 
used in a score test.

Sometimes the biological assays fail.  And so my missing values problem 
is doubled by this transformation (if X1 is missing, but not X2, both 
Xsum and Xdiff will be missing).  So I wanted to -impute- the missing 
values for each Xsum and Xdiff, conditional on all A1-Q2.

Quite possibly the imputation would be not much different if I used 15 
rather than 17 predictor variables; I will investigate using regress.  
But as all these biochemical measures relate to the same biochemical 
pathway, I am not clear how to select "the best" 15 out of 17.

C.
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