I've not done it myself, and this may well be obvious
to those who know the literature, but surely more can
be said.
Missingness can always be represented by a dummy. So
the structure of missing data can always be explored by
logit regression with missingness on something as response
w.r.t. various predictors, which may well include missingness
on some other things as dummy predictors.
Some recent books have touched on graphics for missingness,
too:
Cook, D. and Swayne, D.F. 2007. Interactive and dynamic graphics for
data analysis: With R and GGobi.
New York: Springer.
Unwin, A., Theus, M. and Hofmann, H. 2006. Graphics of large datasets:
Visualizing a million. New York:
Springer.
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Rodrigo A.
Alfaro
Sent: 01 December 2007 21:21
To: [email protected]
Cc: [email protected]
Subject: st: Re: Missing values test
Hi,
As far I know MAR is an assumption.
Joe Schafer's webpage could help you
Rodrigo.
----- Original Message -----
From: "Constantin Colonescu" <[email protected]>
To: <[email protected]>
Sent: Saturday, December 01, 2007 3:51 PM
Subject: st: Missing values test
> Hello,
>
> I run OLS on a list of 10 regressors and the cumulated missing values
> pile up to about 30 percent of my sample. I know I can use -ice- and
> -impute- to generate the missing values, but I would like to make sure
> that my missing values are MRA (missing at random). I was unable to
> find out how to test for MRA. Can anyone help me? Thanks.
>
> Constantin Colonescu
> Grant MacEwan College
> Edmonton
> *
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