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
<https://urldefense.proofpoint.com/v2/url?u=http-3A__error.sd&d=CwMFaQ&c=WO-RGvefibhHBZq3fL85hQ&r=EwICq0J5pL8CwgEJz8qkmauGonk0XmiLpxcYOEgk2a0&m=1PZK_dz_3XlQ8Yml5HzwzZTMpzZoAqGD3DoCZYVAmGM&s=TvjWmjFLHNVK-WvaeeQjpuIAFv4OvMg7RmOVV7vERDo&e=>
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
<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.mattblackwell.org&d=CwMFaQ&c=WO-RGvefibhHBZq3fL85hQ&r=EwICq0J5pL8CwgEJz8qkmauGonk0XmiLpxcYOEgk2a0&m=1PZK_dz_3XlQ8Yml5HzwzZTMpzZoAqGD3DoCZYVAmGM&s=843lXZd8Nax5M8DjLdgn3O92GJ9kusLrEIxKRhz0myU&e=>
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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