Hi, I am trying to use the cem function in R followed by the k2k function, because I have a very large dataset with few treated (about 20,000) and roughly ten times as many not treated respondents (200,000). For this reason I would like to reduce the dataset so that each treated is matched to one non treated person who is similar with respect to certain control variables. However, the k2k function does not seem to work and trying out the vignette suggests that the problem is not specific to my application: When I tried to run the simple example suggested in the vignette, k2k does not work either and of course following estimation commands simply reproduce the normal cem results after the k2k function is used unsuccessfully: data(LL) # cem match: automatic bin choice mat <- cem(treatment="treated", data=LL, drop="re78") mat mat$k2k # ATT estimate att(mat, re78 ~ treated, data=LL) # transform the match into k2k mat2 <- k2k(mat, LL, "euclidean", 1) mat2 mat2$k2k # ATT estimate after k2k att(mat2, re78 ~ treated, data=LL) When I run it, I get an error message for the k2k() function: There were 50 or more warnings (use warnings() to see the first 50) the warnings() function then tells 50 times: 1: In min(x, na.rm = TRUE) : no non-missing arguments to min; returning Inf … 50: In min(x, na.rm = TRUE) : no non-missing arguments to min; returning Inf I also exchanged "euclidean" for method = NULL, and here the 50 warnings read similar: 1: In min(x, na.rm = TRUE) : no non-missing arguments to min; returning Inf ... The concrete output is: G0 G1 All 425 297 Matched 222 163 Unmatched 203 134 [1] FALSE G0 G1 All 425 297 Matched 222 163 Unmatched 203 134 Linear regression model on CEM matched data: SATT point estimate: 550.962564 (p.value=0.368242) 95% conf. interval: [-647.777701, 1749.702830] There were 50 or more warnings (use warnings() to see the first 50) G0 G1 All 425 297 Matched 222 163 Unmatched 203 134 [1] FALSE G0 G1 All 425 297 Matched 222 163 Unmatched 203 134 Linear regression model on CEM matched data: SATT point estimate: 550.962564 (p.value=0.368242) 95% conf. interval: [-647.777701, 1749.702830] I also had a friend trying out the vignette on his macbook and he had the same results. I have a macbook too, if that is of any importance. I would be very glad for help with this problem. Kind regards, Merlin -- Dr. Merlin Schaeffer Wissenschaftszentrum Berlin für Sozialforschung (WZB) Department "Migration, Integration, Transnationalization" Reichpietschufer 50 10785 Berlin, Germany Phone: + 49 30 25491-459 Fax: + 49 30 25491-452 http://www.wzb.eu/en/persons/merlin-schaeffer