Hello,
I am currently using the most recent version of AmeliaView, and I seem
to be encountering a problem with setting the data with trends specific
to each cross-sectional unit. I experience this issue with my datasets
as well as the demo datasets. For example, with freetrade dataset, I set
splines to 3 and checked the drop list for 'Interact Spline with Cross
Section?' However, the log file below shows that, while splinetime=3,
intercs=FALSE. My understanding with this setting is that the setting
should read intercs=TRUE. Please advise.
amelia(x = getAmelia("amelia.data"), m = 5, idvars = NULL, ts = "year",
cs = "country", priors = NULL, lags = NULL, empri = 0, intercs =
FALSE,
leads = NULL, splinetime = 3, logs = NULL, sqrts = NULL,
lgstc = NULL, ords = NULL, noms = NULL, bounds = NULL,
max.resample = 1000,
tolerance = 1e-04)
Eric L. Sevigny, Ph.D.
Assistant Professor
Department of Criminology and Criminal Justice
University of South Carolina
1305 Greene St
Columbia, SC 29208
(803) 777-7043 (office)
sevigny(a)mailbox.sc.edu <mailto:sevigny@mailbox.sc.edu>
Hi
I am struggling with imputations that are allowed to vary between cross sections
(intercs=TRUE). Introducing polynomials works fine, and the algorithm
converges, but as soon as I impose the additional restriction of different
trends between cross sections, this is no longer the case.
The problem with my data set is that for certain variables there are no
observations in particular cross sections. I assume that Amelia thus has
nothing to model the trend in this particular cross section on and therefore
the algorithm can no longer reach convergence.
Is there any way round this problem in Amelia? I fear that imposing the same
trends for all cross-sections would be a bit unrealistic.
Thank you very much for your help!
Best wishes
Florian Reiche
______________________________
Florian Reiche
PhD Researcher and Associate Tutor
Department of Politics
University of Sheffield
Elmfield, Northumberland Road
Sheffield S10 2TU
Homepage: http://www.shef.ac.uk/politics/research/phd/florianreiche.html
** Contact and Feedback Hours: Monday, 11h-12h, Elmfield, room 1.41 **
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I am interested in performing multiple imputation for a data set that
contains sampling weights. (In other words, each subject in the data has
a sampling weight that must be taken into account when fitting
regressions, etc) I did not find any information on this in the Amelia
documentation. Is it possible to do this with Amelia?
-Kurt Smith
--
Kurt Smith, PhD
Scientist II
Archimedes Inc
201 Mission Street, 29th Floor
San Francisco, CA 94105
415.490.0591
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I have data from an experiment with a control and four treatments. There
are several independent and dependent variables, and all have missing data
(which, for the sake of argument, assume occurs randomly). The independent
variables can each be imputed from other independent data *across* the
entire experiment. But the dependent variables, I take it, should be
imputed only from other data *within* each condition. Is that right? Is
there some way to specify this within Amelia? If not, do you recommend
imputing all the independent variables across conditions, then merging them
with dependent variables imputed from within each condition?
--
Donald Braman
http://ssrn.com/author=286206http://www.culturalcognition.net/braman/http://www.law.gwu.edu/Faculty/profile.aspx?id=10123
