Dear Jason,
In MatchIt, you would have to run matchit() separately to each imputed
data set: currently, there is no automated procedure. We will put this on
our to-do list. However, after getting 5 matched data sets for each
imputed data set, you can do the analysis via Zelig, which will adjust
standard errors automatically: see the last example at
http://gking.harvard.edu/zelig/docs/Examples.html
Kosuke
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Kosuke Imai Office: Corwin Hall 041
Assistant Professor Phone: 609-258-6601
Department of Politics eFax: 973-556-1929
Princeton University Email: kimai(a)Princeton.Edu
Princeton, NJ 08544-1012
http://imai.princeton.edu
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On Mon, 20 Feb 2006, Jason Barabas wrote:
Hi,
The MatchIt manual recommends multiple imputation in section 5.2.5 on
page 32. Is it possible to use multiple datasets generated by Amelia in
MatchIt?
Naming each dataset individually does not work (see Example 1 below).
Similarly, the "data=list(...)" method of using multiple datasets in
zelig does not work (see Example 2 below). Finally, I tried to create a
new data frame called "allimputeddata.data" with the variables from each
imputed dataset, but I receive an error message stating: Error in
eval(expr, envir, enclos) : object "outcome" not found. I suspect I
receive this message because the new data frame automatically renames
the variables so that the outcome variables are now
impute1.data.outcome, impute2.data.outcome, etc.
Example 1: m.out <- matchit(outcome ~ var1 + var2 + var3,
data=impute1.data, impute2.data, impute3.data, impute4.data,
impute5.data, method = "nearest")
Example 2: m.out <- matchit(outcome ~ var1 + var2 + var3,
data=list(impute1.data, impute2.data, impute3.data, impute4.data,
impute5.data), method = "nearest")
Example 3: m.out <- matchit(outcome ~ var1 + var2 + var3,
data=allimputeddata.data), method = "nearest")
Thanks for your help.
Jason Barabas
<mailto:matchit@mail-1.hmdc.harvard.edu>
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