I am trying to install Amelia. When I try to intsall I get the following
error message:
> install.packages("Amelia",repos="http://gking.harvard.edu")
Warning: unable to access index for repository
http://gking.harvard.edu/bin/macosx/universal/contrib/2.4
Warning in download.packages(pkgs, destdir = tmpd, available = available, :
no package 'Amelia' at the repositories
Any help greatly appreciated.
Lindsay
Dr Lindsay Stirton
The Norwich Law School and ESRC Centre for Competition Policy,
University of East Anglia,
Norwich, Norfolk, NR4 7TJ
United Kingdom
l.stirton(a)uea.ac.uk
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Hello,
I just wanted to let the list know that we've recently updated Amelia to
correctly handle saving to the Stata 7/8 format. In previous releases, if
AmeliaView was set to save as Stata 7/8, it would finish its run without
saving or giving an error message. Sorry for any inconvenience and please
let us know if you're having any more problems.
You can get Amelia v1.1-20 from http://gking.harvard.edu/amelia or by
running the following line of code in R:
install.packages("Amelia", repos="http://gking.harvard.edu")
Thanks,
matt blackwell.
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Hi,
I wonder why I get error message when I select a polytomous nominal variable
to be overimputed.
The error message is: "The variable you selected doesn't exist in the Amelia
output because it wasn't imputed".
The selected variable had already missing values and was imputed for sure!
I appreciate your response.
saeid
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Hello,
I am new to using Amelia. I have two questions. First, when I run
the overdispersed start values diagnostic, it converges to two
different modes. Adding priors didn't resolve the problem, so I
deleted some variables that were highly correlated and it converged to
a single mode. I was hesitant to remove the variables since I want to
include as many variables in the imputation model as possible. Is
there another way of managing this kind of situation? Unfortunately,
I have a high rate of missingness for many variables and a small
sample size of 208 countries. Originally, I had 31 variables, but
after deleting 6 highly correlated variables, I now have 25 variables,
3 of which are completely observed. I realize that this means I have
more parameters than observations, but I don't know what else to do
when I can't add any more countries to the sample. With a ridge prior
of 1, the imputations seemed to run well, however.
Second, with my smaller data set that appears to run well, as soon as
I begin running the overdispersed starting values diagnostic, the
first imputed data set (outdata1.csv) disappears from the folder
leaving only data sets 2-10. Is this an error or should it be deleted
for purposes of a burn-in? If this is not an error, should I run 11
imputations to get 10, not using the first data set? Alternatively,
should I make a copy of the data sets before running the diagnostics
and use the first imputed data set? This has not happened in previous
imputation models that I have run, so I have re-run the same model
three times and the first dataset appears after running the
imputations, but is disappears once I begin running diagnostics. The
overdipersed start values diagnostic graph shows that the values do
not converge until around 1,000 imputations. I increased the ridge
prior to 2 and the same thing happened.
I appreciate your response.
Erin Saunders
Portland State University
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Hi,
I would like to know why Amelia keeps giving me error when I try to impute
missingness in a variable (set to nominal) in a dataset consisting of three
variables (sex set to nominal and age to numerical both without missing
values). I also tried sex and age groups as nominal but it didn't help. I
appreciate your replay to my email.
Thanks
Saeid
Harvard Initiative for Global Health
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Hi Amelia-folks, I have a few questions regarding variable transformations
in the imputation models:
1. Do you I need to use the same variable transformations in my analysis
model as in the imputation model? If, for example, I use the quadratic root
transformation to normalize a couple of event count variables, do I have to
use the same root transformed variables in my analysis model or could I use
the "raw" variables?
2. I have a couple of variables (e.g., oil exports as percentage of GDP)
that are highly positively skewed, containing many zeros but with a long
tail. What kind of transformation would be advisable in that case (I gather
the natural log might not be a good idea since there are so many zeros;
moreover, the results look weird if I log these variables)?
3. I have a variable (a democracy index) which ranges from 0-10 and is
pretty severely bimodal, that is, with most of the cases falling at either
low or high values. What kind of transformation could help normalize this
variable?
4. Relatedly, my dependent variable is the annual change (first difference)
of this democracy index. The imputation model does an extremely poor job at
predicting this variable (judging from the overimputation plot), however,
which makes substantively sense in light of my analytical results. Would a
better idea then be to impute the level and lagged level of this variable,
then compute the annual change variable from these imputed variables, and
run the analysis?
All the best,
Jan Teorell
Docent, Associate Professor of Political Science
Department of Political Science, Lund University
Box 52, SE-221 00 Lund, SWEDEN
Ph: +46 46 222 8093 Email: jan.teorell(a)svet.lu.se
Hi, I'm grand new at using AMELIA, so this question is possibly stupid.
I cannot locate the new imputed datasets made by AMELIA II (I run the Amelia
View version). I run the session and get no error mesages, I can diagnose
the results but nowhere on my computer do I find any files with the name I
put in the "Name Output Dataset" field, nor any new datafile (I want them in
Stata 7/8 format).
Any clues?
A related question is whether I can determine myself in what directory the
new datasets will be stored?
All the best,
Jan Teorell
Docent, Associate Professor of Political Science
Department of Political Science, Lund University
Box 52, SE-221 00 Lund, SWEDEN
Ph: +46 46 222 8093 Email: jan.teorell(a)svet.lu.se
If your covariates don't predict well, the key is that the imputations
have enough variability to represent that fact. Amelia will normally do
this right. It may be in your application that you wind up with very
large standard errors and confidence intervals, but they would in that
case be accurate. If that's the case, you could try finding better
variables (perhaps coded as a function of the existing ones) or finding
better data. Not much else you can do...
Gary
p.s. the list address is Amelia Listserv <amelia(a)lists.gking.harvard.edu>
not owner-amelia_at_lists_gking_harvard_edu(a)mail.hmdc.harvard.edu
On Fri, 9 Feb 2007, Christopher Parker wrote:
> To whom it may concern,
>
> I wrote a while back to ask about using MI, much in the same way that
> Scheve (2006) did in recent SAPD paper in which he imputed data for a
> variable that was not observed in the dataset of interest.
>
> Here's the question. I'm attempting to expmine the effects of mental
> health on politics. It's a revisitation of some of Lasswell's work. As you
> know, there's nothing in the way of data on this. So, I'm trying to use MI
> as a temporary solution. I plan to use data from a national survey on
> mental health, the National Comorbidity Study, to fill in the
> "missingness" within the NES for depression, anxiety, and PTSD.
>
> The problem is that there are only 30 variables, all of which are
> demographic in some way, that overlap between the two datasets and can be
> used as information. Moreover, when I tried to predict depression,
> anxiety, and PTSD in the NCS, the r-squares average .05. Perhaps this has
> something to do with the fact that the NCS has 9K observations and over 1K
> variables. That said, I'm concerned that once imputed into the NES the
> mental health items will be too noisy to predict anything. Is this
> something about which I need to be concerned, i.e., the low explained
> variances?
>
> Any advice is welcomed.
>
> Best,
> Christopher
>
>
>
>
>
>
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17 years, 2 months
1
0
by Christopher Parker
auth e9a2b8314b1a8b61 subscribe amelia_at_lists_gking_harvard_edu
parker(a)berkeley.edu
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Hello,
I just wanted to update the list to say that we've recently cleared up
some bugfixes, many of which were related to nominal variables. If you've
had problems with nominal variables in the past, you may want to grab the
latest version of Amelia (1.1-18) by running the following line of code at
the R command line:
install.packages("Amelia", repos="http://gking.harvard.edu")
You can also download the Windows executable setup file from the Amelia
website (http://gking.harvard.edu/amelia/), which will install the latest
version.
Thanks for using Amelia and please let us know if you have any problems.
cheers,
matt.
============
Matt Blackwell
blackwel(a)fas.harvard.edu
Department of Government
Harvard University
Cambridge, MA 02138
============
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