After updating to the newest version of Amelia (1.7.4), I tried
overimputing a dataset that has incorrect values in one of its variables.
All of the error observations are measured identically (as zeros, where
they should be positive). The code I originally used is below, and it
triggers a warning of the type: "Some observations estimated with negative
measurement error variance. Set to gold standard."
dat<-data.frame(A, B, C, VS)
mopd<-moPrep(dat, VS~VS, subset=VS<.0001)
I looked through the github code as to what causes this error (other than,
of course, the negative error variance), and more importantly, how to
activate the gold.standard (which for my purposes is the rest of the values
for VS) and presumably fix this issue. After trying quite a few different
possible codings, I can't get it to work. I either receive the same error,
or a host of errors surrounding how I've included gold.standard in the
code. I would think it should be easy, since I'm basically bifurcating my
data (all data under some amount is the subset measured with error; all
data over the amount can be considered gold-standard data), but can't
figure it out. Thanks for any help you can give,
Sean
Hello,
I'm having trouble getting started with Amelia.
I'm using Ameliaview 1.7.4
The data I'm using comes from ANES, in Stata form.
When I try to load my modified data set into Amelia (via the import Stata tab) all I get is a "failure in loading data" message.
I then was successfully able to load into Amelia the unaltered data set from ANES.
This data set had several administrative variables, that did not vary, so Amelia asked me to remove them.
Is there a way to do this from within Amelia? As after I deleted the offending variables outside of Amelia, I again was faced with the "failure in loading data message"
So I guess I have a number of questions.
Is there any reason my stata data set won't load into Amelia? Linked to this does Amelia only work on unaltered data sets?
Finally, is there anyway to delete variables from within Amelia?
many thanks
John Megson
I have 761,592 obs for 31 variables on users behaviours towards online ads.
Out of 31 variables, 28 are categorical. Many cat. variables have more than
10 categories. I am using Amelia for missing data imputation.
It's taking very long time. Are there other ways to do it fast? What's the
Amelia limits on number of observations ?
Is there any R-package which perform better on large dataset for missing
data imputation?
I checked for complete cases, there are only 172 complete cases which is
very insignificant as compare to total dataset.
--
Mithilesh Kumar