1 Jun
2016
1 Jun
'16
7:54 a.m.
Hi Huiying,
Question 1: MO wasn't really designed for binary variables with measurement
error and so there isn't really a proper way to specify the prior mean/SD.
If you have validation (gold-standard) data, you include both the validated
and misclassified/mismeasured variable in the same amelia model and get
imputations that way. Otherwise, you could try to force amelia to handle
this situation by setting the prior mean to either 0 or 1 with some SD that
reflects your uncertainty and then manually force the imputations to be
either 0 or 1 after the imputations.
Question 2: moPrep is a convenience function that sets up the priors and
overimp arguments in the right way. So you should get the same imputations
either way (except for the randomness of the imputations themselves).
Cheers,
Matt
~~~~~~~~~~~
Matthew Blackwell
Assistant Professor of Government
Harvard University
url: http://www.mattblackwell.org
On Tue, May 31, 2016 at 5:21 AM, HuiYing Chua <hyc.tartoon(a)gmail.com> wrote:
Hi there,
I have basically 2 questions related to setting observation-level priors
on nominal variables.
I am trying to do an overimputation on a dichotomous variable, say y1.
My 1st question:
I am aware that using the argument “priors” and “overimp”, I could specify
observation-level priors by 4-column matrix (row, column, prior.mean,
prior.sd) or 5-column matrix (row, column, lower confidence range, upper
confidence range, confidence level). I am attempting the 4-column matrix
but I am not sure how do I specify prior.mean and prior.sd when my prior
is the dichotomous variable itself. I read somewhere prior.mean can be set
to y1 itself? Is prior.sd similar to the proportion of variance
attributable to measurement error? Would need advice on how do I specify
prior.sd in this case.
My 2nd question:
I am also aware of generating prior using the command “moPrep” from the
Amelia package. The argument “error.proportion” from “moPrep” command is
rather easy to understand (proportion of variance attributable to
measurement error). But what is the difference setting priors using
“moPrep" and “priors”? Should the output be the same?
Please kindly advice. Many many thanks !
Huiying
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