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@Princeton.Edu] Sent: Thursday, April 19, 2012 20:24 To: McDonald, Robert J., M.D., Ph.D. Cc: matchit(a)lists.gking.harvard.edu 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/