1 Jun
2016
1 Jun
'16
10:40 a.m.
Hi Skip,
I think that averaging across imputations for quantiles (including the
median) is valid. What we are getting in this case is the posterior mean of
the complete variable median (averaging over the distribution of the
missing data):
rowMeans(sapply(imp$imputations, function(x) apply(x,2, median))
It's a bit odd to think of, but this will work. It is, of course,
difference than the posterior median of the complete variable median, which
you could also calculate:
apply(sapply(imp$imputations, function(x) apply(x,2, median), 2 median)
Cheers,
Matt
~~~~~~~~~~~
Matthew Blackwell
Assistant Professor of Government
Harvard University
url: http://www.mattblackwell.org
On Wed, Jun 1, 2016 at 9:24 AM, Skip Barbour <russellbarbour(a)gmail.com>
wrote:
Matt,
Thanks.. what about for pooled median values? Using this code for median
"averages " across imputations would be inappropriate, no?
Skip
On Tue, May 31, 2016 at 4:43 PM, Matt Blackwell <
mblackwell(a)gov.harvard.edu> wrote:
Hi Skip,
Unfortunately, there is no canned way to get all summary statistics, but
you can use the "mi.meld" function to take a list of quantities of interest
(e.g., means) and their SEs and combine them using the Rubin rules. If you
don't care about the uncertainty and just want to average the quantities,
then you can use these two bits of code:
## print the estimated means for each variable
rowMeans(sapply(imp$imputations, colMeans))
## print the estimated SDs for each variable
rowMeans(sapply(imp$imputations, function(x) apply(x,2,sd))
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=XjMRIoymm8BVqt4u3ZotvsQDHbh0k-IQboHm7mDGfJI&s=WlRtifgeZNP04r6SF8anqZ3dZR2ooecjAuoL_9lTZ-Q&e=>
On Tue, May 31, 2016 at 12:29 PM, Skip Barbour <russellbarbour(a)gmail.com>
wrote:
--
*There is nothing so fatal to character as half finished tasks.*
David Lloyd George
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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*There is nothing so fatal to character as half finished tasks.*
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