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