http://blog.gmane.org/gmane.comp.lang.r.matchit hourly 1 1901-01-01T00:00+00:00 http://gmane.org/img/gmane-25t.png http://gmane.org http://comments.gmane.org/gmane.comp.lang.r.matchit/313 <pre>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 - --- MatchIt mailing list served by HUIT List Address: matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org Subscribe/Unsubscribe: http://lists.gking.harvard.edu/mailman/listinfo/ei MatchIt Software and Documentation: http://gking.harvard.edu/matchit/ Browse/Search List Archive: http://lists.gking.harvard.edu/mailman/private/matchit/ Matchit mailing list Matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org https://lists.gking.harvard.edu/mailman/listinfo/matchit </pre> Francesco 2012-04-27T17:30:40 http://comments.gmane.org/gmane.comp.lang.r.matchit/310 <pre>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 - --- MatchIt mailing list served by HUIT List Address: matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org Subscribe/Unsubscribe: http://lists.gking.harvard.edu/mailman/listinfo/ei MatchIt Software and Documentation: http://gking.harvard.edu/matchit/ Browse/Search List Archive: http://lists.gking.harvard.edu/mailman/private/matchit/ Matchit mailing list Matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org https://lists.gking.harvard.edu/mailman/listinfo/matchit</pre> McDonald, Robert J., M.D., Ph.D. 2012-04-19T16:55:55 http://comments.gmane.org/gmane.comp.lang.r.matchit/303 <pre>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 &lt; Pr(T=1 | X) &lt; 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 - --- MatchIt mailing list served by HUIT List Address: matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org Subscribe/Unsubscribe: http://lists.gking.harvard.edu/mailman/listinfo/ei MatchIt Software and Documentation: http://gking.harvard.edu/matchit/ Browse/Search List Archive: http://lists.gking.harvard.edu/mailman/private/matchit/ Matchit mailing list Matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org https://lists.gking.harvard.edu/mailman/listinfo/matchit </pre> georgebaah 2012-04-05T15:37:02 http://comments.gmane.org/gmane.comp.lang.r.matchit/298 <pre> 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 &lt;- with(lalonde, cbind(age, educ, black, hispan, married, nodegree, re74, re75)) # GET ESTIMATE FROM MATCHIT() set.seed(5678) m.out1 &lt;- 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 &lt;- 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 &lt;- 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)$est # NOTE DISCREPANCY BETWEEN ncol(X) AND length(weights) # There are only 8 variables in X, so why is matchit() producing weights for nine variables? ncol(X) # 8 length(weights) # 9 # ADD PROPENSITY SCORE TO X AND RE-ESTIMATE WITH MATCH() glm1 &lt;- glm(treat ~ age+educ+black+hispan+married+nodegree+re74+re75, family=binomial, data=lalonde) X2 &lt;- with(lalonde, cbind(age, educ, black, hispan, married, nodegree, re74, re75)) X2 &lt;- cbind(glm1$fitted, X2) Match(Y=lalonde$re78, Tr=lalonde$treat, X=X2, estimand="ATT", Weight.matrix=diag(weights), ties=TRUE)$est # 939.2 - --- MatchIt mailing list served by HUIT List Address: matchit&lt; at &gt;lists.gking.harvard.edu Subscribe/Unsubscribe: http://lists.gking.harvard.edu/mailman/listinfo/ei MatchIt Software and Documentation: http://gking.harvard.edu/matchit/ Browse/Search List Archive: http://lists.gking.harvard.edu/mailman/private/matchit/ Matchit mailing list Matchit&lt; at &gt;lists.gking.harvard.edu https://lists.gking.harvard.edu/mailman/listinfo/matchit</pre> John G. Bullock 2012-03-15T16:48:01 http://comments.gmane.org/gmane.comp.lang.r.matchit/296 <pre>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 -- *Gary King* - Albert J. Weatherhead III University Professor - Director, IQSS - Harvard University GKing.Harvard.edu &lt;http://gking.harvard.edu/&gt; - King at Harvard.edu&lt;https://lists.gking.harvard.edu/mailman/listinfo/matchit&gt; - &lt; at &gt;kinggary&lt;http://twitter.com/kinggary&gt;- 617-500-7570 - Asst 495-9271 - Fax 812-8581 On Mon, Feb 27, 2012 at 12:14 PM, Donny Baum &lt;donnybaum at gmail.com&lt;https://lists.gking.harvard.edu/mailman/listinfo/matchit&gt;&gt; wrote: Lloyds TSB Bank plc. Registered Office: 25 Gresham Street, London EC2V 7HN. Registered in England and Wales, number 2065. Telephone: 020 7626 1500. Bank of Scotland plc. Registered Office: The Mound, Edinburgh EH1 1YZ. Registered in Scotland, number 327000. Telephone: 0870 600 5000 Lloyds TSB Scotland plc. Registered Office: Henry Duncan House, 120 George Street, Edinburgh EH2 4LH. Registered in Scotland, number 95237. Telephone: 0131 225 4555. Cheltenham &amp; Gloucester plc. Registered Office: Barnett Way, Gloucester GL4 3RL. Registered in England and Wales, number 2299428. Telephone: 01452 372372. Lloyds TSB Bank plc, Lloyds TSB Scotland plc, Bank of Scotland plc and Cheltenham &amp; Gloucester plc are authorised and regulated by the Financial Services Authority. Halifax is a division of Bank of Scotland plc. Cheltenham &amp; Gloucester Savings is a division of Lloyds TSB Bank plc. HBOS plc. Registered Office: The Mound, Edinburgh EH1 1YZ. Registered in Scotland, number 218813. Telephone: 0870 600 5000 Lloyds Banking Group plc. Registered Office: The Mound, Edinburgh EH1 1YZ. Registered in Scotland, number 95000. Telephone: 0131 225 4555 This e-mail (including any attachments) is private and confidential and may contain privileged material. If you have received this e-mail in error, please notify the sender and delete it (including any attachments) immediately. You must not copy, distribute, disclose or use any of the information in it or any attachments. Telephone calls may be monitored or recorded. - --- MatchIt mailing list served by HUIT List Address: matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org Subscribe/Unsubscribe: http://lists.gking.harvard.edu/mailman/listinfo/ei MatchIt Software and Documentation: http://gking.harvard.edu/matchit/ Browse/Search List Archive: http://lists.gking.harvard.edu/mailman/private/matchit/ Matchit mailing list Matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org https://lists.gking.harvard.edu/mailman/listinfo/matchit</pre> Adamakis, Sotirios (Customer Analytics & Decision 2012-02-28T12:48:21 http://comments.gmane.org/gmane.comp.lang.r.matchit/293 <pre>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 </pre> Donny Baum 2012-02-27T17:14:16 http://comments.gmane.org/gmane.comp.lang.r.matchit/292 <pre>Thank You very much Kosuke, I will check my variables right now. May I ask you a little methodological question ? When I use the (logit) propensity matching method , the average propensity score for treated is about 12%... because only a small part of the treated has scores between 50% and 80%... Does it means that there is an omitted variable somewhere ? Or there is no absolute reference level and I have to assess this figure relatively to the average score of the control group? I was expecting to obtain a propensity score much higher for the treated ...  also because when I run the same logistic specification of the probability of bein treated using another stat package (in order to see the significance of the coefficients) I obtain that all my variables are highly significant at the 1% level... What do you think ? Many thanks On 27 February 2012 04:36, Kosuke Imai &lt;kimai&lt; at &gt;princeton.edu&gt; wrote: - --- MatchIt mailing list served by HUIT List Address: matchit&lt; at &gt;lists.gking.harvard.edu Subscribe/Unsubscribe: http://lists.gking.harvard.edu/mailman/listinfo/ei MatchIt Software and Documentation: http://gking.harvard.edu/matchit/ Browse/Search List Archive: http://lists.gking.harvard.edu/mailman/private/matchit/ Matchit mailing list Matchit&lt; at &gt;lists.gking.harvard.edu https://lists.gking.harvard.edu/mailman/listinfo/matchit</pre> Marco Francesco 2012-02-27T11:33:04 http://comments.gmane.org/gmane.comp.lang.r.matchit/290 <pre>Dear Matchit list, I am using matchit for my work, and I really appreciate the excellent work you have done so far. I have a question : I perform a nearest neighbor matching procedure with a large dataset ( 40 000 individuals, 15 variables) and when I use the standard propensity score as a distance, everything works fine : the matching is quite good However if I specify the "mahalanobis" distance I get an error saying that : "Lapack dgesv : le système est exactement singulier" (the system is exactly singular)... Do you have an idea of what might cause this problem ? I have no missing data... Many thanks - --- MatchIt mailing list served by HUIT List Address: matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org Subscribe/Unsubscribe: http://lists.gking.harvard.edu/mailman/listinfo/ei MatchIt Software and Documentation: http://gking.harvard.edu/matchit/ Browse/Search List Archive: http://lists.gking.harvard.edu/mailman/private/matchit/ Matchit mailing list Matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org https://lists.gking.harvard.edu/mailman/listinfo/matchit</pre> Francesco 2012-02-24T22:32:01 http://comments.gmane.org/gmane.comp.lang.r.matchit/285 <pre>Hi,   I'm trying to implement the example at page 18 from the MatchIt manual. The example is about a way to estimate the ATT.   I can't implement the example because I'm using model="ls.mixed" so my dataframe has to be in a long format. In concrete terms, when I extract my dataframe with match.data() to restructure it in long format, I can't use the option data=match.data(mydata, "control") within Zelig.   Is there a workaround ? I tried to use matchit() with the long format dataframe, but it gives wrong mathching since each case has multiple observations in that format.   Thanks, François Maurice- --- MatchIt mailing list served by HUIT List Address: matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org Subscribe/Unsubscribe: http://lists.gking.harvard.edu/mailman/listinfo/ei MatchIt Software and Documentation: http://gking.harvard.edu/matchit/ Browse/Search List Archive: http://lists.gking.harvard.edu/mailman/private/matchit/ Matchit mailing list Matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org https://lists.gking.harvard.edu/mailman/listinfo/matchit</pre> Francois Maurice 2012-02-01T19:16:33 http://comments.gmane.org/gmane.comp.lang.r.matchit/282 <pre>Hi,   I'm using MatchIt. I'm trying to understand which method produce ATE estimate and which one produce ATT estimate.   In the documentation, section 5: Frequently Asked Questions: How Exactly are the Weights Created?, it is said :   "These weights are constructed to estimate the average treatment effect on the treated, [...]".   Is there a way with MatchIt to estimate ATE ? To be concrete, I'm using experimental data with a control group almost three times the treated group. I'm using the following four methods with ratio=2 in matchit():   Nearest : Drop some controls Subclassification : Keep all controls Nearest with exact : Drop some controls Genetic : Drop some contrls   Since subclassification keeps all controls, can that be an ATE estimate or do I need to built my own weights to make sure it is ATE?   In general, is MatchIt produced only match set with weights that can only be use to estimate ATT ?   And if I use Zelig after MatchIt, is there a way to produce ATE estimate ?   Thanks,   François Maurice- --- MatchIt mailing list served by HUIT List Address: matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org Subscribe/Unsubscribe: http://lists.gking.harvard.edu/mailman/listinfo/ei MatchIt Software and Documentation: http://gking.harvard.edu/matchit/ Browse/Search List Archive: http://lists.gking.harvard.edu/mailman/private/matchit/ Matchit mailing list Matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org https://lists.gking.harvard.edu/mailman/listinfo/matchit</pre> Francois Maurice 2012-02-01T16:38:42 http://comments.gmane.org/gmane.comp.lang.r.matchit/281 <pre>Hi, again! I am comparing optimal matching to two nearest neighbor matching methods. I ran the PSM in MatchIt! and I am seeing some weird stuff in the distributions of my covariates for the optimal method. The standardized mean differences are very low on average for my simulation runs. However the treated and control groups ore very dissimilar in the optimal matching method. The covariate distributions in the nearest neighbor methods are very similar. How can I explain that? Thanks! Shane Phillips - --- MatchIt mailing list served by HUIT List Address: matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org Subscribe/Unsubscribe: http://lists.gking.harvard.edu/mailman/listinfo/ei MatchIt Software and Documentation: http://gking.harvard.edu/matchit/ Browse/Search List Archive: http://lists.gking.harvard.edu/mailman/private/matchit/ Matchit mailing list Matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org https://lists.gking.harvard.edu/mailman/listinfo/matchit</pre> Shane Phillips 2012-02-01T15:34:25 http://comments.gmane.org/gmane.comp.lang.r.matchit/280 <pre>Good morning! I used MatchIt! to run a simulation comparing three different types of propensity score matching techniques: 1-to-1 Nearest neighbor including exact matching on two dichotomous variables, 1-to-1 nearest neighbor with a .1 sd caliper, and 1-to-1 optimal matching. After conducting 1000 runs of 1600 cases each (134 treated cases and 1466 possible control cases), optimal showed the lowest average standardized mean difference, but there was MUCH more variability in the standardized mean difference values than in the other two methods. How can I explain this? All of the methods used the same data. There was not much competition for controls. The nearest neighbor methods used the default order settings. Please help!!!! Thanks, Shane Phillips - --- MatchIt mailing list served by HUIT List Address: matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org Subscribe/Unsubscribe: http://lists.gking.harvard.edu/mailman/listinfo/ei MatchIt Software and Documentation: http://gking.harvard.edu/matchit/ Browse/Search List Archive: http://lists.gking.harvard.edu/mailman/private/matchit/ Matchit mailing list Matchit-boHuZhbfW+Y6DHxCPSz5rL5+avTOL6Rl&lt; at &gt;public.gmane.org https://lists.gking.harvard.edu/mailman/listinfo/matchit</pre> Shane Phillips 2012-02-01T12:03:33 http://comments.gmane.org/gmane.comp.lang.r.matchit/276 <pre>Folks, This is not a matchit question, but I wondered if I could access your wisdom. Suppose there is a panel dataset, and different units get the treatment at different dates. That is, there isn't a sharp date on which the treatment is applied; different units get the treatment on different dates. We felt the right estimation strategy would be: 1 Identify the units that never got treated; they're the control pool 2 Walk through time. In each time period, identify the firms who are treated. Find controls using propensity score matching where information from that time period (only) is used. Once a control has been used, flag it so it will never be used again. 3 In this fashion, for each unit that's treated at time t, find a control at that time t. 4 Shift everything into event time where the treatment date is defined as 0 5 Proceed with the analysis as usual. Would appreciate your insights. </pre> Ajay Shah 2012-01-01T17:01:50 http://comments.gmane.org/gmane.comp.lang.r.matchit/271 <pre>Hi! How would I go about estimating ATE in R following full matching and optimal matching in MatchIt? Would it be any different than the method shown in the documentation for nearest neighbor matching? Thanks! Shane </pre> Shane Phillips 2011-09-20T00:10:14 http://comments.gmane.org/gmane.comp.lang.r.matchit/266 <pre>Hi! I am trying to calculate ATT for some data, and I am getting an error. Below is the relevant code, the error and the traceback... znn.out&lt;-zelig(growthz~age+cogat+iep*(mapfall+itbs)+lunch*(mapfall+itbs)+treat, data=match.data(nearest), model="ls") nnx.out &lt;- setx(znn.out, data = match.data(nearest,"treat"), cond = TRUE) snn.out &lt;- sim(znn.out, x = nnx.out) Error in mvrnorm(num, mu = coef(object), Sigma = vcov(object)) : incompatible arguments&gt; traceback()6: stop("incompatible arguments") 5: mvrnorm(num, mu = coef(object), Sigma = vcov(object)) 4: param.lm(object, num = num, bootstrap = bootstrap) 3: param(object, num = num, bootstrap = bootstrap) 2: sim.cond(znn.out, x = nnx.out) 1: sim(znn.out, x = nnx.out) Note: Age, cogat, mapfall, and itbs are continuous variables. Iep, lunch, and treat are binary. Please help! S </pre> Shane Phillips 2011-09-09T14:56:13 http://comments.gmane.org/gmane.comp.lang.r.matchit/258 <pre>Hello everyone, I was wondering if the package has a function to perform incidence density sampling (case-control sampling). Tony </pre> Galois Theory 2011-09-06T17:12:00 http://comments.gmane.org/gmane.comp.lang.r.matchit/255 <pre>Dear Matchitlist, Many thanks for that incredible and useful software that solves (almost) all my matching problems ... I would like to know if it is possible to use something similar to the "exact" option in nearest neighbor matching for all the others matching algorithms.. That is, for example, apply a genetic algorithm but being able to obtain matches that have absolutely the same values for, lets say, their date of birth or any other string or numeric variable... Many thanks in advance, Best </pre> John Litfiba 2011-08-29T13:30:13 http://comments.gmane.org/gmane.comp.lang.r.matchit/253 <pre>Hi, MatchIt Gurus... I am running a simulation using MatchIt as part of my dissertation. I am trying to compile a dataset of some of the summary data from the MatchIt procedure (e.g. summary(df, standardized=TRUE)), but I seem to be unable to do that. I keep getting the message that that object cannot be coerced into a dataframe. Is there anyway that I can export these statistics so that I can compile the results over many repetitions? Thanks! Shane </pre> Shane Phillips 2011-08-27T00:07:16 http://comments.gmane.org/gmane.comp.lang.r.matchit/251 <pre>First my complements to all contributors of MatchIt, you really made my life easier! Now to my question. I estimated a variety of propensity scores by playing with several different models: GBM, Logit and then trying out several matching techniques: Nearest Neighbour (with a variety of calipers), Full Matching, Subclassification and Genetic Matching. In the literature I found &lt;http://www.sciencedirect.com/science/article/pii/S1010794009005727&gt;researchers to assess the balance by comparing the standardized difference in means and calculating the variance ratio. MatchIt summary output shows the standardized difference in means being most effectively reduced with 1:1 Matching with a Caliper of 0.25 (all covariates below a 10% level). Since the t-test was understandably removed in later versions I wonder if the variance ratio suffered the same fate? Or in other words why is it not included in the output? And would it be possible to generate it from the output? And do you perhaps have any other pointers on how to assess balance or generally accepted values that indicate a good balance? Greatly appreciated if someone could point me in the right direction. Regards, Paul </pre> Paul Diterwich 2011-08-23T15:37:39 http://comments.gmane.org/gmane.comp.lang.r.matchit/249 <pre>Hi,  I'm trying to implement the ideas in Harder, Stuart and Anthony (2010), to seperate the estimation step from the application step and that way to combine various mathcing methods with various distance measures.   But I have a problem with genetic mathing. Is there a way to obtain distance values from genetic mathcing (method="genetic") ? The distance values for the genetic method obtain from the matchit() object are the same as those when choosing method="nearest" and distance="logit" (the final results are not the same).   Thanks, François Maurice, B. Sc., A. Stat. Candidat à la maîtrise Département de sociologie Université de Montréal</pre> Francois Maurice 2011-08-14T15:37:44 http://comments.gmane.org/gmane.comp.lang.r.matchit/243 <pre>Hi there, Could you please tell me how is the calculation of the std.mean.diff in the matchit output summary programmed- what is the exact formula used for this standardization (is it standardized according to the SDc and SDt group?) Many thanks, Best regards, Ana</pre> Ana Kolar 2011-08-12T19:37:44 Search the mailing list at Gmane query http://search.gmane.org/?group=$group=gmane.comp.lang.r.matchit