thanks for your note. yes, relogit in Zelig will do everything you're
interested in and so you don't need to calculate C or the var-cov matrix
yourself; all the corrections are made for you.
the key is to recognize that there are 2 separate forms of bias
correction, one for case-control studies, for which you'd use the
case.correct option, and the other for rare events bias (which is
appropriate whether or not you use case control designs), for which you'd
use the bias.correct option.
Best of luck with your research,
Gary
---
Gary King,
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
On Tue, 10 Jan 2006, Dave Armstrong wrote:
> Dear Prof King,
>
> I read your paper "Logistic Regression in Rare Events Data" and have just a
> couple of quick questions about it. For consistency of notation, I've put
> them in the attached PDF. If you would have some time to look at them or
> point them and me in the right direction, it would be very much appreciated.
>
>
> Thanks for your help,
>
> Dave.
>
> --
> Dave Armstrong
> University of Maryland
> Dept of Government and Politics
> 3140 Tydings Hall
> College Park, MD 20742
> Office: 2103L Cole Field House
> Phone: 301-405-9735
> e-mail: darmstrong(a)gvpt.umd.edu
> web: www.davearmstrong-ps.com
>
> Facts are meaningless. You can use facts to prove anything that's even
> remotely true. - Homer Simpson
>
> To this day, philosophers suffer from Plato's disease: the assumption that
> reality fundamentally consists of
> abstract essences best described by words or geometry. (In truth, reality is
> largely a probabilistic affair best
> described by statistics) - Steve Sailer "The Unexpected Uselessness of
> Philosophy"
>
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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
-----------------------------------------------------
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
-----------------------------------------------------
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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