Hello,
I am using Amelia to impute missing values in two large data files and a
third smaller file. The smaller file, which contains 19 variables and 866
cases, imputed fairly easily and quickly both with and without
transformations. The two larger files are another story completely. One of
them, which has 87 variables and 16,491 cases, was imputed over the course
of several hours when I set Amelia only to transform the continuous
variables with skewed distributions. However, when I tried to set Amelia to
also transform all nominal and ordinal variables, I received this error
message:
Error in La.svd(x, nu, nv) : error code 1 from Lapack routine 'dgesdd'
I decided to set only some of the ordinal and nominal variables to
transform, and Amelia began its work. Well, it's been working for 84 hours
and it's only on imputation 2! Imputation 1 had a chain length of 266 and
imputation 2 is currently at 229.
I also set another large file, with 286 variables and 3,305 cases, to be
imputed by Amelia on a different computer. I started the imputation process
at roughly the same time (84 hours ago) and it appears that it has finally
made it to imputation 3, with chain lengths of over 2,000 for the first two
imputations.
Also, I had downloaded the newer Amelia program this past Saturday but it
would not run these larger files--it appeared to freeze when I hit "impute."
So I went back to the earlier version of Amelia I had downloaded in January,
and this version is imputing the large data files, but obviously very
slowly.
Could there be something wrong with my settings causing this extreme
slowness? Or does it simply take this long to impute large data files?
Thanks,
Mari Cunnington
Doctoral Student
Teachers College, Columbia University
--
-Mari
Hi Everyone,
I used amelia to run an imputation and I was wondering if there was a
standard on how consistant the chain lengths should be for each imputation.
I did 10 imputations, and some ranged between 10-12 iterations while other
took between 180-400. The tolerance was .001 and I set the ridge prior to be
5% of the observations. Are these differences ok or do I need to change
something to try and get the imputations more equal. The data set has 127
observations with 6 measures that are repeatedly measured over 3 time
points.
Thanks for your help!
Sivan
Hi all,
I've a question about multiple imputation for a data set that will be later
analysed using a selection models. I am re-analysing the chapter 5 of
Przeworski at all Democracy and Development, on the effects of political
regime on demography, where the authors use selection models (dynamic probit
version of the Heckman models) to account for regime selection effects.
There is a massive number of missing data in their analysis (sometimes they
use less than a 1/3 of the observations). However, my main concern is that
both, the missing data mechanism and the selection process are not enough
independent.
>From my understanding both selection models and multiple imputation are
trying to account for missing data, but, perhaps, in different ways. Yet, it
is not clear to me how to compare them. One way is this: Multiple imputation
is trying to help us is using all information available in the data set,
without creating actually new information. In fact the missing cells should
be fulfilled based on the information available from the other cells. On the
other hand, selection models are actually supposed to generate new data, as
if there is no selection process going on and thus the assignment of the
treatment and the control groups are actually random. If this is the right
direction about how to think, then maybe running selection models after
multiple imputation with Amelia would not be a problem. But I am not
sure...any suggestions?
Help and advice really appreciated,
Best,
Antonio.
Hi,
I am trying to run a multiple imputation but I keep getting this error
message:
There was an unexpected error in the execution of Amelia.
Double check all inputs for errors and take note of the error message:
Error in if (ncol(timevars)) { : argument is of length zero
I have run this file before and only changed one variable so I am not sure
what is happening.
--
Thank you for the help,
Sivan Rotenberg
Doctoral Student
Concordia University