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
