Unfortunately, I don't think we have an automated procedure for everything. You would have to multiply impute the data, do matching on each imputed data set, and then combine it in zelig() using mi() function. But this does not require any programming. You can simply run the same matching procedure on each data set via matchit() and then feed the resulting multiple matched data sets into zelig().
Good luck,
Kosuke
Department of Politics
Princeton University
http://imai.princeton.edu
On Sep 13, 2011, at 6:02 PM, Pingaul jb wrote:
> Dear Professor,
> I’m a post-doctoral student at Montreal University. I’m actually in Columbia, working and propensity scores with a colleague and using MatchIt and Zelig. First, congratulations for your packages that are very flexible.
>
> My question is about multiple imputation and propensity scores with these softwares. From what I understand, combining both approaches would include:
>
> 1/ Doing multiple imputation and testing which variables to include.
>
> 2/ Propensity score analysis on each imputed data set and pooling the overall balance to check if it is ok (or on each data set?).
>
> 3/ Calculation of the quantities of interest for each data set
>
> 4/ Pooling the quantities across data sets.
>
> I would like to know if there is a written syntax to perform the MatchIt analysis for all of the imputed data set without having to do it manually and check the overall balance. Also, in theory, the number of individuals retained after propensity score matching and the weights can be different for each imputed data set. So that we have to perform the final analysis on each one and then pool the data with a specific procedure to take into account the eventual varying Ns? I normally use Mice package for multiple imputation but it seems that Zelig handle Amelia. My colleague seems to do be able to do all that in stata, but I’m not sure how to make all the three R packages work together.
>
> I would be very happy if you could indicate to me a reference or a place where I can find the syntax to do that (I’ve been using R for some times so I can use packages easily but I have no programming skills).
>
>
> Best Regards!
>
>
>
> Jean-Baptiste
>
-
Zelig Mailing List, served by Harvard-MIT Data Center
Send messages: zelig(a)lists.gking.harvard.edu
[un]subscribe Options: http://lists.gking.harvard.edu/?info=zelig
Zelig program information: http://gking.harvard.edu/zelig/
*Please, let me know how can I install ZeligMultinomial package. I want to
use mlogit, which according to the manual (page 50), is found in said
package.
I tried with the command,
install.packages("ZeligMultinomial", repos="http://r.iq.harvard.edu/",
type="source")
But received the following message:
Warning: dependency 'MNP' is not available
trying URL '
http://r.iq.harvard.edu/src/contrib/ZeligMultinomial_0.5-4.tar.gz'
Content type 'application/x-gzip' length 9730 bytes
opened URL
==================================================
downloaded 9730 bytes
During startup - Warning messages:
1: Setting LC_CTYPE failed, using "C"
2: Setting LC_TIME failed, using "C"
3: Setting LC_MESSAGES failed, using "C"
4: Setting LC_PAPER failed, using "C"
ERROR: dependency 'MNP' is not available for package 'ZeligMultinomial'
* removing
'/Library/Frameworks/R.framework/Versions/2.14/Resources/library/ZeligMultinomial'
The downloaded packages are in
'/private/var/folders/UL/ULLu+bi5GR8IM1G-7XZBJU+++TI/-Tmp-/Rtmp6jw6M9/downloaded_packages'
Warning message:
In install.packages("ZeligMultinomial", repos = "http://r.iq.harvard.edu/",
:
installation of package 'ZeligMultinomial' had non-zero exit status
***
Dear All,
I am using Zelig to perform a simple logistic regression but with multiple imputation (so feeding zelig with multiple data sets). I could not find online how to:
1. Obtain a display of confidence intervals. I tried confint() that I used on glm() fitted objects but it does not seem to work with a Zelig object fitted on multiple data sets.
2. Assess multicollinearity. i was thinking of calculating VIFs for every data set and then average them but I am not sure it is correct to simply average them and I wonder if there is something easier already implemented in Zelig.
Many Thanks!
Jean-Baptiste Pingault
The University of Montreal
Hello,
I would like to calculate an average treatment effect on treated cases using
Matchit and Zelig in R. Last week I ran into a problem when Matchit produces a
weighted file rather than a 1 to 1 match. I worked out the follwoing work around
in R, that was working last week. I was able to obtain the conditional
treatments effects I was seeking.
logv =c(FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE
,TRUE, TRUE, TRUE, FALSE)
msmatch <- matchit(contew ~ blk + hisp+ white + nFRM0910 + nELL0910 + ZMath0910
+ ZRead0910 + ZWrite0910 + gr3 + gr4 + gr5 + ngend0910,data=msdata,
method="genetic",replace =TRUE,ratio=2, exact=logv,pop.size=700,estimand="ATT")
msmatchdata <- match.data(msmatch)
z.out <- zelig(ZRead1011 ~ ZMath0910 + ZRead0910 + ZWrite0910 ,
data = match.data(msmatch, "control"), model = "ls", weights="weights")
z.out2 <- zelig(ZRead1011 ~ ZMath0910 + ZRead0910 + ZWrite0910 ,
data = match.data(msmatch, "control"), model = "ls")
z.out$call<-z.out2$call
summary(z.out)
x.out1 <- setx(z.out, data = match.data(msmatch, "treat"), cond = TRUE)
s.out <- sim(z.out, x = x.out1)
summary(s.out)
It looks my Zelig version was updated as now when I run the same code I get the
warning message:
In setx.default(z.out, data = match.data(msmatch, "treat"), cond = TRUE) :
"cond" is not currently supported by this version of Zelig
Is anyone familiar with the reason why conditional prediction is no longer
supported with the more recent Zelig version?
Many thanks in advance!
Hi,
I am new to Zelig and wondering if relogit can be used with random effects.
I could not find any statement on this in the documentation.
Thank you,
Jerg
