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
>
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*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
***
I used the install method you suggested and was successful, but the version
that was installed didn't include about half of the models that Zelig uses
(e.g., Relogit).
Thanks
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Hi there,
I'm fitting a bivariate logistic (blogit) model using Zelig and would like to use the equations given in the documentation to hard code some predictions, and to understand further how the model works. The problem is that while it is easy to calculate the marginal probabilities, it is unclear from the documentation what value of psi to use in the equations for the joint probabilities. The documentation gives:
Odds ratio (psi )= exp(x3*beta3)
But we don't know what to substitute for x3 in this equation. Using exp((intercept):3) from the model does not give plausible answers, which leads us to assume we are missing something obvious and important.
Have searched the archives and also the VGAM documentation with no luck, so would be grateful for any pointers.
R2.15.0 on Windows 7.
Thanks in advance for any help.
Regards,
David Auty
Stagiaire Postdoctoral
Département des sciences du bois et de la forêt
Université Laval
Québec (QC) G1V 0A6
