If you know the values of Age, then sure I'd fill them in before Amelia. if
you are imputing based on smoothness or something like that, you might want
to consider some of the time series (or time series, cross-section)
features.
on the error message: imputation algorithms like Amelia require lots more
observations than variables, and if you do, then the issue is only how you
have the data organized perhaps; i'd run some descriptive stats just to be
sure.
Pls don't forget my coauthors, James Honaker and Matt Blackwell too!
Gary
---
http://gking.harvard.edu
On Wed, Jan 13, 2010 at 11:58 AM, Peter Flom <
peterflomconsulting(a)mindspring.com> wrote:
Hello,
I just downloaded Amelia (and Zelig a little while ago). They look great!
I'm a statistician/data analyst, mostly working for psychologists, social
scientists, doctors, etc.
My question:
I have a data set with 4 time points. The DV is a count, very
overdispersed and right skew ... it's a number of unprotected sexual acts.
IV's are marital status, age, income, and highest grade of school. Various
patterns of missingness.
My questions:
1) Age clearly increases in a known pattern. It doesn't really need to be
imputed. Can I make this happen in Amelia, or should I do it separately,
before running Amelia?
2) I tried running
susanMI.out <- amelia(susan, m = 5, ts = "time", noms = "married",
cs =
'id', intercs = T, polytime = 1)
and got an error
The number of observations is too low to estimate the number of
parameters. You can either remove some variables, reduce
the order of the time polynomial, or increase the empirical prior.
which is clear, but I am not sure what to do here.
Thanks for any advice, and thanks to Gary King for writing the software!
Peter
Peter L. Flom, PhD
Statistical Consultant
Website:
http://www DOT statisticalanalysisconsulting DOT com/
Writing;
http://www.associatedcontent.com/user/582880/peter_flom.html
Twitter: @peterflom
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