On 04/26/05 09:58, Giordano Sanchez wrote:
Hello,
Thanks for the instructive responses. But two questions arise.
Firstable I can't manage to load the library "mice".
I'm using R 2.0.1 on my Debian
The package called norm also has functions for missing data.
When I tried it, the values it gave were not sensible for my
problem, but I may have done something wrong. (This was a simple problem that did not involve multiple imputation.)
The second question is more statistical:
aregImpute() seems to give good results but i would like to compare the
different methods not just graphically. It'is possible?
What different methods? Compare how? Are you assuming that we remember your last post?
I also have other meteorological stations that have correleted data with the data station I'm using? Can I use those data to improve my imputation method.
This sounds like exactly what aregImput() is good for, or
transcan(), depending on whether you need to make inferences (and hence do multiple imputation).
Jon
For those interested I have preprints of a paper comparing MICE, aregImpute, and transcan on the basis of simulations.
--
Frank E Harrell Jr Professor and Chair School of Medicine
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