2 Sep
2009
2 Sep
'09
4:16 p.m.
In general (of course there will be exceptions), do you recommend imputing
each missing value to produce a single data set with imputations rather or
stripping out missing cases?
On Wed, Sep 2, 2009 at 11:37 AM, Gary King <king(a)harvard.edu> wrote:
On 09/02/2009 12:20 AM, Donald Braman wrote:
I can't seem to subscribe to the MatchIt
list, so I'm posting here in
hopes that the same people read both.
I'm working with multiply imputed data from Amelia & wondering if any
progress has been made at integrating Amelia, MatchIt, and Zelig -- in
particular, pre-processing multiply imputed data and preparing them for use
in Zelig.
Not at all pressing, and (again) apologies for posting here rather than
the MatchIt list.
Cheers, Don
At the moment, MatchIt doesn't allow missing data on input (unless you
temporarily code it as observed and exact match on the missing values). In
part, this is because most matching algorithms don't have procedures to deal
with missingness. An exception is CEM which has a procedure for multiply
imputed data, but to access that you'll need to use the (R or Stata) version
of CEM directly (see http://gking.harvard.edu/cem) instead of the one in
MatchIt.
(separate message coming to fix your matchit subscription...)
Gary
---
Gary King
Albert J. Weatherhead III University Professor
Director, Institute for Quantitative Social Science
Harvard University, 1737 Cambridge St, Cambridge, MA 02138
http://GKing.Harvard.Edu, King(a)Harvard.Edu
Direct 617-495-2027, Assistant 495-9271, eFax 812-8581