Is there a way of getting the pooled descriptive statistics from the
imputed datasets? I guess this must be spelled out somewhere, but
reviewing the Amelia and Zeplig documentation I have not yet found it.
Sorry for asking such a basic question
Skip Barbour
On Thu, Jan 21, 2016 at 11:05 PM, Matt Blackwell <mblackwell(a)gov.harvard.edu
wrote:
> Hi Sean,
>
> Apologies for taking so long to get back to you on this. I think what you
> are trying to accomplish is less for multiple overimputation. What moPrep
> is trying to do here is use the variance of the mismeasured observations
> relative to the variance of the gold standard observations. But if all of
> your mismeasured observations have 0 variance (since they are all 0!) then
> this strategy won't work. Thus, you can do one of two things:
>
> 1) Provide a standard error of the measurement error for those
> observations (using the error.sd argument)
>
> 2) Simply set those observations to NA and impute those observations like
> usual in amelia() (possibly with bounds argument to make sure they will be
> positive)
>
> Hope that helps!
>
> Cheers,
> Matt
>
> ~~~~~~~~~~~
> Matthew Blackwell
> Assistant Professor of Government
> Harvard University
> url:
http://www.mattblackwell.org
>
> On Wed, Dec 30, 2015 at 2:24 PM, Sean Kates <sk5350(a)nyu.edu
wrote:
>
>> 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
>>
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