gmane.comp.lang.r.matchit

customising the plot() command

Francesco — 2012-04-27T17:30:40

Dear Matchit list,

I have a simple question : how can I customize the output of the plot command ?
I am thinking of changing the title,  choosing only  particular
covariates in the QQ plots, etc ...

Many thanks for your help!
Best Regards
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Re: Converting match matrix results to single columngroup?

McDonald, Robert J., M.D., Ph.D. — 2012-04-20T13:13:49

Kosuke,

Thanks for the email.  Here is a means to generate a matching number
using some additional R code:

len <- dim(original.data)[1]
match <- rep(NA,len)
len2 <- length(propensity.data$match.matrix)
count <- 1
for(i in 1:len2){

match1 <- propensity.data$match.matrix[i]
match2 <- row.names(propensity.data$match.matrix)[i]

if(!is.na(match1)){
match[as.numeric(match1)] <- count
match[as.numeric(match2)] <- count
count <- count+1}

}

original.data$match <- match
propensityout.data$match <-
match[as.numeric(row.names(propensityout.data))]

#Where: original.data is the complete datafile prior to running MatchIt
#Where: propensity.data is the raw output using MatchIt 
#Where: propensityout.data is from propensity.out <-
match.data(propensity.data)

This loop does not rank the matches based upon propensity score of the
initial 
paring partner in the match.matrix. Out of curiosity, will wfe or some
other 
conditional logistic regression require this pairing to be ranked/sorted
with 
respect to the propensity score? If so, I will need to append the code.


How do other people "use" the output from MatchIt that lacks an appended

subcategorization column (such as subclass or the matching number
above)?
Perhaps I missed a simple way to perform subsequent analyses using an
unmanipulated version of the match.matrix where the paring partner
identities are important?  Most of the conditional logistic regression
packages I have found do not seem permit a matrix as the conditional
variable.  I am still new to R syntax so it is possible I may be
overlooking something.

Thanks,

Bob

 

-----Original Message-----
From: Kosuke Imai [mailto:kimai-cTYGs6MY1n4q5t9Oc1ygMg< at >public.gmane.org] 
Sent: Thursday, April 19, 2012 20:24
To: McDonald, Robert J., M.D., Ph.D.
Cc: matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl< at >public.gmane.org
Subject: Re: [matchit] Converting match matrix results to single column
group?

Unfortunately, you would have to do a bit of programming for this I
think: writing a loop etc.  Once you create this variable, however, the
analysis can be done simply by running the weighted fixed effects
regression.  Doing the difference-in-means within each strata and then
aggregating is the same as running the weighted fixed effects regression
with certain regression weights: see this paper
http://imai.princeton.edu/research/FEmatch.html (section 2.2).  The
weighted fixed effects can then be run with the software "wfe", which we
developed. 

Kosuke

Department of Politics
Princeton University
http://imai.princeton.edu

On Apr 19, 2012, at 12:55 PM, McDonald, Robert J., M.D., Ph.D. wrote:

1:1 matching (eventually 1:N) from MatchIt to both assess the
improvement in covariate balance and to analyze outcomes.  Is there any
way, using R, to append the match.matrix to the matched dataset as a new
column that represents matched pair group numbers (where each grouping
represents the two members of the pair?  I plan to use this as a means
to perform the conditional logistic regression, conditioned on the
matched pair group number.
http://lists.gking.harvard.edu/mailman/listinfo/ei
http://lists.gking.harvard.edu/mailman/private/matchit/

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Re: Converting match matrix results to single columngroup?

Kosuke Imai — 2012-04-20T01:23:31

Unfortunately, you would have to do a bit of programming for this I think: writing a loop etc.  Once you create this variable, however, the analysis can be done simply by running the weighted fixed effects regression.  Doing the difference-in-means within each strata and then aggregating is the same as running the weighted fixed effects regression with certain regression weights: see this paper http://imai.princeton.edu/research/FEmatch.html (section 2.2).  The weighted fixed effects can then be run with the software "wfe", which we developed. 

Kosuke

Department of Politics
Princeton University
http://imai.princeton.edu

On Apr 19, 2012, at 12:55 PM, McDonald, Robert J., M.D., Ph.D. wrote:


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Converting match matrix results to single column group?

McDonald, Robert J., M.D., Ph.D. — 2012-04-19T16:55:55

I wish to perform conditional logistic regression on the results of 1:1 matching (eventually 1:N) from MatchIt to both assess the improvement in covariate balance and to analyze outcomes.  Is there any way, using R, to append the match.matrix to the matched dataset as a new column that represents matched pair group numbers (where each grouping represents the two members of the pair?  I plan to use this as a means to perform the conditional logistic regression, conditioned on the matched pair group number.

Thanks for your help,

Bob
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Re: matchit() silently adds a propensity score to X whenmethod=='genetic'

Kosuke Imai — 2012-04-10T21:04:23

Rocio,

I'm speaking without knowing the details of the numerical algorithm used by GenMatch, but I don't think GenMatch will assign zero weights to the estimated propensity score when the propensity score model is misspecified. There are two reasons for this. First, the propensity score is likely to be correlated with covariates and usually a parametric model like the one GenMatch is unable to assign zero weight to only one of several highly correlated variables without some kind of explicit regularization for variable selection. Second, it is not even clear to me whether it is desirable to assign zero weight to the misspecified propensity score. If matching balances the covariate distribution (not just marginal means etc.), then by definition the treatment should be independent of *any* function that involves pre-treatment covariates, which includes the misspecified propensity score. What Marc Ratkovic and I do in our covariate balancing propensity score is that we estimate the propensity score such that the resulting propensity score maximizes balance (measured in terms of moments of some functions of covariates selected by researchers) as well as predicts the treatment assignment (the definition of propensity score). My understanding is that CEM avoids the problem by having the researchers bin the covariate space and balancing the covariate distributions in this coarsened covariate space. Jen's entropy weighting and Marc's SVM matching basically do the same thing. I think that the issue of which functions (or bin sizes) of covariates you choose is an open question. There are two ways to deal with this, I think. The first is a nonparametric approach. I'm playing with this but at this point it is not clear to me whether it works sufficiently well in practice. For any nonparametric method, the choice of bandwidth parameter is always an issue too. The second is to specify an outcome model or at least use the knowledge of the outcome variable in some creative ways. I've seen some working papers that try this, but I don't know how well these approaches work in general.

Best,
Kosuke

Department of Politics
Princeton University
http://imai.princeton.edu

On Apr 10, 2012, at 4:11 PM, Gary King wrote:

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Re: matchit() silently adds a propensity score to X whenmethod=='genetic'

Gary King — 2012-04-10T20:11:51

This all seems reasonable, but nothing in this is guaranteed without a
theorem we'd all like but doesn't exist.   So without that, everything -
including all claims in your message - may or may not hold in any one data
set.  If you're finding it works well for your data, then that's terrific.
  Since you can't guarantee that it will work the next time, you just have
to check after the fact whether balance is achieved, tweak the model or
algorithm, and then rerun, check balance, and continue to iterate.

You can avoid the iteration with CEM <http://gking.harvard.edu/cem>, since
the max imbalance in that approach is set by the user before matching, and
there are theorems to back this up.   Kosuke also has a covariate balancing
pscore <http://imai.princeton.edu/research/CBPS.html> approach that uses
the pscore for weighting instead of matching that seems to work well. There
are lots of other approaches too.

Best,
Gary
--
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IQSS - Harvard University
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< at >kinggary<http://twitter.com/kinggary>- 617-500-7570 - Asst 495-9271 -
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On Tue, Apr 10, 2012 at 10:16 AM, Rocio Titiunik <titiunik-63aXycvo3TyHXe+LvDLADg< at >public.gmane.org> wrote:

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Re: matchit() silently adds a propensity score to X whenmethod=='genetic'

Rocio Titiunik — 2012-04-10T14:16:14

Hi Gary,

Sorry for my extremely tardy response -- I've been traveling a lot and
could not find the time to continue our conversation. But I am
responding now, on the off chance that you still remember what our
conversation was about!

To answer your question, no, I have not run simulations to come to
this conclusion. What I said is based on my understanding of the
GenMatch algorithm and the explanation on the paper by Diamond and
Sekhon in the Review of Economics and Statistics
(http://sekhon.berkeley.edu/papers/GenMatch.pdf)

There are two parts to what I said. One is that matching on a
miss-specified propensity score will not necessarily lead to matched
samples where balance is better than, say, in the unmatched data. This
is a statement that is unrelated to GenMatch, it applies in general,
and is based on the fact that the theorem in Rosenbaum and Rubin 1983
is about the true propensity score. But I think we are not disagreeing
about this.

If I understand your comment, the discussion is about the part of my
statement where I said:

As I said, I have not run simulations to show this, although I think
it would be great to have such simulations and perhaps I'll find some
time to work on them. My statement was based on my knowledge of the
algorithm: GenMatch finds matches by minimizing a generalized
Mahalanobis distance, and finds weights so that this distance will
produce high-balance matches when minimized. The weights are chosen so
that balance is maximized, and if the algorithm finds that giving
positive weights to one or more of the variables in X creates worse
balance than giving zero weights to them, then these variables will be
given zero weights (technically, very small but positive weights so
that the weight matrix is positive definite). So, incorporating a
miss-specified propensity score may, in the beginning, make balance
worse, but eventually the algorithm will figure out that the
miss-specified propensity score should not be given positive weight.

Now, the key is the phrase "eventually", because it's impossible to
anticipate when this will happen, whether it will be feasible to wait
enough generations to get there, etc. So it is possible that one stops
the algorithm before the zero-weight-to-pscore solution has been
found. Similarly, if one fails to include the propensity score and the
propensity score is correctly specified, GenMatch should be able to
find the weights so that matching on X without the pscore leads to
same degree of balance, but it can of course happen that in some
problems the number of generations that the algorithm has to run to
find the optimal weights is too large and therefore not feasible in
practice.

My point was simply that although we often do see in applications that
adding an estimated propensity score to GenMatch helps to find better
balance, this is not true in general. And, actually, this statement
applies to any nearest neighbor matching algorithm, because we can
never be sure that the propensity score we added was the correct one.
Do you or Kosuke disagree with this statement? If so, I'll be very
interested in reading your thoughts about it. Again, I think the idea
of having simulations to show this is great, as it will be very useful
to characterize, if at all possible, the cases where adding an
estimated propensity score might be detrimental. I will be sure to
share them with list if I get around to do them.

Cheers,
Rocío

--
Rocío Titiunik
Assistant Professor
Department of Political Science
University of Michigan
http://www.umich.edu/~titiunik/

On Sat, Mar 17, 2012 at 10:13 AM, Gary King <king-nRXb03v5zC6Vc3sceRu5cw< at >public.gmane.org> wrote:

Re: Question

Kosuke Imai — 2012-04-06T15:22:48

Not at all. You don't want to condition on the observed outcome, which is different from potential outcomes. 

Kosuke Imai
Princeton University
http://imai.princeton.edu

Sent from my iPhone

On Apr 5, 2012, at 1:20 PM, georgebaah <baah-/4noJB3qBVQ3uPMLIKxrzw< at >public.gmane.org> wrote:

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Re: Question

georgebaah — 2012-04-05T17:20:14

Hmmm, interesting. Maybe I am thinking too much.
Let me put it this way. Suppose Y takes on two values y1, y2 and
  0 < Pr(Y|T) < 1 but say Pr(T=1|Y=y1) = 1.0.  Does this violate the  
potential outcome model?

George
On Apr 5, 2012, at 12:50 PM, Olivia Lau wrote:


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Re: Question

Olivia Lau — 2012-04-05T16:50:44

Think of it this way:

If T were independent of Y | X, that is like saying that you expect the
treatment to have no effect on the outcome Y, conditional on background
characteristics.  If this were the case, then the treatment T would be
considered a failure in the real world, right? :)


On Thu, Apr 5, 2012 at 8:37 AM, georgebaah <baah-/4noJB3qBVQ3uPMLIKxrzw< at >public.gmane.org> wrote:

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Question

georgebaah — 2012-04-05T15:37:02

Hi Everyone,
   I have a theoretical question. It's been bothering me for a while.
I have a dataset from which I know the treatment is stochastic: 0 <  
Pr(T=1 | X) < 1.
Also I know that Y is independent of T given X. However, T is not  
independent
of Y given X. Does this make sense? Does this violate the theory behind
the potential outcome model? Thanks.

George 
  
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Re: matchit() silently adds a propensity score to X whenmethod=='genetic'

Gary King — 2012-03-17T14:13:55

Hi Rocío & John, after a nudge from Kosuke, I have a question.  Did you run
some type of simulation to come to the conclusion in your last paragraph,
or can you describe the data sets where it worked?   Earlier versions of
this method did not have the property you describe in example data sets we
tried, it is usually easier and faster to produce better balance with other
methods, and of course no theoretical result guarantees that the claims in
your paragraph are general.  So it would be nice to know the types of data
sets where it worked the way you describe.   If you can eventually figure
out how to more formally characterize in what types of data it works as you
describe, that might be very useful.
Gary
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On Fri, Mar 16, 2012 at 1:24 PM, Rocio Titiunik <titiunik-63aXycvo3TyHXe+LvDLADg< at >public.gmane.org> wrote:

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Re: matchit() silently adds a propensity score to X whenmethod=='genetic'

Gary King — 2012-03-17T01:54:08

sounds like GenMatch has improved since I tried it last.  that's great.
 it's a good idea.   thanks, we'll fix the manual.
Gary
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On Fri, Mar 16, 2012 at 1:24 PM, Rocio Titiunik <titiunik-63aXycvo3TyHXe+LvDLADg< at >public.gmane.org> wrote:

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Re: matchit() silently adds a propensity score to X whenmethod=='genetic'

Rocio Titiunik — 2012-03-16T17:24:33

Thanks for your reply Gary.

Adding a propensity score to GenMatch is usually a good idea in
applications, and indeed it's recommended in Diamond and Sekhon
(2012). But we were concerned about replicability, because we thought
there was an error in our code when we couldn't replicate the matchit
results using GenMatch directly.

To continue the conversation, whether GenMatch finds or does not find
balance does not depend on whether one adds a propensity score to the
matching matrix. If simple matching on the propensity score leads to
perfect balance, then GenMatch will give the propensity score maximum
weight and will give zero weight to the rest of the components of the
matching matrix. In these cases, if one fails to include the
propensity score, GenMatch will still find the same solution, but it
will take longer than if the propensity score had been included. But
the opposite can also happen: if matching on the propensity score does
not improve balance at all, GenMatch will eventually figure this out
and assign zero weight to it, but it will take longer to find balance
than if the propensity score had not been included in the first place.
In other words, the Rosenbaum and Rubin theorem holds for the true
propensity score. Matching on a badly misspecified propensity score
will not improve balance, and in these cases including it in GenMatch
will slow the balance-finding process.


Rocío

Re: matchit() silently adds a propensity score to X whenmethod=='genetic'

Gary King — 2012-03-16T13:43:46

Thanks John and Rocío, Good catch, we'll include it in the manual.  FYI, as
I recall when we wrote matchit, we added the propensity score to genmatch
because genmatch didn't seem to reduce balance without it.  that's odd
because genmatch already attempts to optimize an arbitrary function of all
the variables in the pscore, and so if its doing what its advertising
adding yet another function of the same variables shouldn't do anything.
But you never know what happens in such a complicated optimization problem,
and the pscore often helped it do a bit better and so we included it.
Gary
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2012/3/15 John G. Bullock <john.bullock-LrD5EImo2rg< at >public.gmane.org>

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matchit() silently adds a propensity score to X whenmethod=='genetic'

John G. Bullock — 2012-03-15T16:48:01

Hello,

     I am writing on behalf of myself and Rocío Titiunik.

     As we see it, matchit() serves as a front end to GenMatch() 
when method=='genetic'.  So it should be possible to get the same 
results from both the MatchIt and Matching packages.  But this 
proves difficult.  Discrepancies seem to occur because matchit() 
silently adds a column of propensity scores to the X matrix that it 
passes to GenMatch().  I'm appending some code that we developed to 
demonstrate this point.

     The benefits (in most cases) of adding a propensity score are 
clear, but it seems problematic that this addition occurs without 
any notification.  For example, a user of MatchIt may claim to have 
matched on a set of predictors, unaware that he has also matched on 
the propensity score.  Or, if a user thinks that he has matched on 
one propensity score (because he included it in X when calling 
matchit()), he will be unaware that he has also matched on a second, 
matchit()-generated propensity score that is based on the first 
propensity score.  In either case, he will be also be unable to 
replicate his MatchIt results with Matching or vice versa, even 
though MatchIt is supposed to be a wrapper for Matching.  And 
third-party readers of code that includes a call to matchit() will 
likely have no idea that a propensity score has been added.

     We can't see any explicit mention of this 
propensity-score-adding feature in the MatchIt manual.  To promote 
future replication efforts, can a line be added to the MatchIt 
manual about the addition of a propensity score to X?  Or can a note 
be added to matchit() output whenever method=='genetic' is used?

Thank you,
John Bullock
###


library(Matching)
library(MatchIt)
data(lalonde)
X <- with(lalonde, cbind(age, educ, black, hispan, married, 
nodegree, re74, re75))

# GET ESTIMATE FROM MATCHIT()
set.seed(5678)
m.out1 <- matchit(treat ~ X, data=lalonde, method='genetic', 
estimand='ATT', ties=TRUE, print.level=1,
                        pop.size=150, wait.generations=1, 
max.generations=10, hard.generation.limit=TRUE,
                        unif.seed=1945, int.seed=1906)

# Create a vector of "parameters at the solution" reported by matchit().
# Behind the scenes, these weights have been produced by GenMatch().
weights <- c(8.121767e+02, 7.735192e+02, 5.938936e+02, 4.079661e+02, 
3.665018e+02, 1.361011e+02,
                  9.644896e+02, 6.083636e+02,  2.346772e+00)

ATT.MatchIt <- with(match.data(m.out1), 
weighted.mean(re78[treat==1], weights[treat==1])) -
                with(match.data(m.out1), 
weighted.mean(re78[treat==0], weights[treat==0]))
print(ATT.MatchIt)  # 939.2


# GET ESTIMATE FROM MATCH()
# The Match() estimate is -952.3 -- very different.
Match(Y=lalonde$re78, Tr=lalonde$treat, X=X, estimand="ATT", 
Weight.matrix=diag(weights), ties=TRUE)$est


# NOTE DISCREPANCY BETWEEN ncol(X) AND length(weights)
# There are only 8 variables in X, so why is matchit() producing 
weights for nine variables?
ncol(X)          # 8
length(weights)  # 9


# ADD PROPENSITY SCORE TO X AND RE-ESTIMATE WITH MATCH()
glm1 <- glm(treat ~ 
age+educ+black+hispan+married+nodegree+re74+re75, family=binomial, 
data=lalonde)
X2 <- with(lalonde, cbind(age, educ, black, hispan, married, 
nodegree, re74, re75))
X2 <- cbind(glm1$fitted, X2)

Match(Y=lalonde$re78, Tr=lalonde$treat, X=X2, estimand="ATT", 
Weight.matrix=diag(weights), ties=TRUE)$est  # 939.2

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Re: Using matchit for large datasets

Gary King — 2012-02-28T15:10:54

eval=FALSE.  If there's an issue, just use the cem package directly.
Gary
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On Tue, Feb 28, 2012 at 7:48 AM, Adamakis, Sotirios (Customer Analytics &
Decision) <Sotirios.Adamakis-cWPatjyaurWQ51x8FlUl6A< at >public.gmane.org> wrote:

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Using matchit for large datasets

Adamakis, Sotirios (Customer Analytics & Decision — 2012-02-28T12:48:21

Hi, I have the same problem. Which option turns off the calculation of L1?

Regards,
Sotiris



________________________________

if you use CEM and turn off the calculation of L1, its very fast and can

deal with very large data sets.



Gary

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On Mon, Feb 27, 2012 at 12:14 PM, Donny Baum <donnybaum at gmail.com<https://lists.gking.harvard.edu/mailman/listinfo/matchit>> wrote:









































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Re: (no subject)

Kosuke Imai — 2012-02-28T03:27:25

From the data, you cannot tell whether there exists an omitted variable.  Also, coefficients and standard errors of the propensity score model do not mean much.  Propensity score is a tool to achieve covariate balance.

Kosuke

Department of Politics
Princeton University
http://imai.princeton.edu

On Feb 27, 2012, at 6:33 AM, Marco Francesco wrote:


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Re: Using matchit for large datasets

Gary King — 2012-02-27T17:57:51

if you use CEM and turn off the calculation of L1, its very fast and can
deal with very large data sets.

Gary
--
*Gary King* - Albert J. Weatherhead III University Professor - Director,
IQSS - Harvard University
GKing.Harvard.edu <http://gking.harvard.edu/> - King-vbSkjhsARtCVc3sceRu5cw< at >public.gmane.org -
< at >kinggary<http://twitter.com/kinggary>- 617-500-7570 - Asst 495-9271 -
Fax 812-8581



On Mon, Feb 27, 2012 at 12:14 PM, Donny Baum <donnybaum-Re5JQEeQqe8AvxtiuMwx3w< at >public.gmane.org> wrote:

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Using matchit for large datasets

Donny Baum — 2012-02-27T17:14:16

Has anyone come across problems of performance using MatchIt with large
datasets? I am trying to perform nearest neighbor matching with 10
subclassifications on a sample of 400,000 (50,000 treatment cases, 350,00
untreated) with about 25 covariates. I was able to get one round of
successful results after about 8 hours of waiting for R to produce the
output. Is this typical of using MatchIt with large data? Is there any way
to increase the speed or otherwise work around this?

Any help would be great.

Cheers,

Don Baum