On 04/13/05 21:05, Chris Bergstresser wrote: This article is great; thanks for providing it. The authors recommend either using "ML Estimation" or "Multiple Imputation" to fill in the missing data. They don't talk much about which is better for certain situations, however.
Multiple imputation is good when you want to make statistical inferences. It is what aregImpute() is good for. I used transcan() for a situation that did not involve inference: Our graduate admissions committee of 5 rates applicants, and the members of the committee differ somewhat in mean and variance, and sometimes a member is out of the room when an applicant is rated. So I attempt to mimic what the member will do anyway, which is to conform and adjust: s.m <- as.matrix(students[,4:8]) # ratings, NA when missing s.imp <- transcan(s.m,asis="*",data=s.m,imputed=T,long=T,pl=F) s.na <- is.na(s.m) # which ratings are imputed s.m[which(s.na)] <- unlist(s.imp$imputed) students[,4:8] <- s.m The last 3 lines seem like a kludge to me, but I couldn't find any other way in the time I had, and this works. This does not involve multiple imputation. I guess it would also be OK for inference if there weren't very many missing data, but don't take my word for it. Jon -- Jonathan Baron, Professor of Psychology, University of Pennsylvania Home page: http://www.sas.upenn.edu/~baron R search page: http://finzi.psych.upenn.edu/ ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html