Hi again
I am making some progress.... but...
My data set has data on 164 women, each measured at 4 time points.
The DV is number of unprotected sexual acts in last 3 months. Since this is a data set of
commercial sex workers, this variable is highly skewed.
The IVs are age, marital status, highest grade of school, and income.
Marital status is (naturally) nominal. The others are numeric.
When I run
susanMI.out <- amelia(susan2, m = 5, ts = "time", noms = "married",
cs = 'id',
intercs = T,
sqrts = "unprot_vag_sex",
polytime = 0)
I get warnings about noninvertible matrices and highly colinear variables - but none are
that highly colinear.
If I run without the polytime option, I get no errors, and the overall distribution of the
variables is pretty good, but the distribution within people is not good at all. That is,
running
tscsPlot(susanMI.out, cs =2, var = "unprot_vag_sex")
shows that where the DV is missing, the imputed values aren't even close to the
(admittedly high) value where it is present. In this case, only one value was present.
But for woman number 8, three values were present, all were 0, but the imputed value for
the missing time was about 35, and the range was 0 to over 100.
Add lags = "unprot_vag_sex" and leads = "unprot_vag_sex", made the
range much smaller, but the values were still very far off.
I had thought that polytime = 0 would set constant values ... but it led to the errors
above.
Thanks in advance for any help and sorry to be so long winded, but I thought all these
details would matter
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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