The EM algorithm does not impute missing data, rather it estimates parameters when you have missing data (those parameters can then be used to impute the missing values, but that is separate from the EM algorithm).
If you create a dataset that has missing values imputed (a single time) and then analyze that dataset as if there were no missing data then your results will be wrong. The better approach is multiple imputation (and there are packages including MICE to do this) where more than one new dataset is imputed (including error on the imputed missing values), then each of the imputed datasets is analyzed (don't look at the results yet, they are still each wrong), then the analyses are combined to give a correct answer (well as correct as any statistical procedure is, approximate is probably the better term). Though this of course is assuming that your assumptions are reasonable. If SPSS really gives you a single imputed dataset after running EM for you to analyze using other tools then my opinion of SPSS will go down. The reason that you probably have not found a way to do this in SAS or R is because they are useful tools that try to not make it easy to do the wrong thing. On Sat, Jul 21, 2012 at 5:55 AM, ya <xinxi...@163.com> wrote: > Hi list, > > I am wondering if there is a way to use EM algorithm to handle missing data > and get a completed data set in R? > > I usually do it in SPSS because EM in SPSS kind of "fill in" the estimated > value for the missing data, and then the completed dataset can be saved and > used for further analysis. But I have not found a way to get the a completed > data set like this in R or SAS. With Amelia or MICE, the missing data set > were imputed a couple of times, and the new imputed datasets were not > combined. I understand that the parameter estimation can still be done in the > way of combination of estimates from each imputed data set, but it would be > more convenient to have a combined dataset to do some analysis, for example, > ANOVA with IVs having more than two categories. In this case, the only way to > get the main effect of the whole IV is to estimate parameters in a single > data set(as far as I know). If the separated imputed data sets were used, > then the main effect showed in the result were for each category of the IV, > respectively. I figured sometimes the readers and reviewers would like to see > how ! bi! > g the effect for the whole IV instead of the effect of each category of that > IV. > > This is one of the reasons I can not fully move to R from SPSS. So any > suggestions? > > Thank you very much. > > > > > ya > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- Gregory (Greg) L. Snow Ph.D. 538...@gmail.com ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
