Dear JoAnn, Thank you very much for your reply. If that is the case I am surprised. I would have though ML could incorporate study cases with some missingness in them. Furthermore I believe ML estimates should generally be more robust than complete case based estimates. For unbiased estimates I think ML requires the data is MAR, complete case requires the data is MCAR Maybe it is more difficult to make the ML estimate on incomplete data than I imagine. My knowledge is patchy.
Thanks again. regards Desmond > Hello Desmond, > > The only way to not drop cases with incomplete data would be some sort > of imputation for the missing covariates. > > JoAnn > > Desmond Campbell wrote: >> Dear all, >> >> I want to do a logistic regression. >> So far I've only found out how to do that in R, in a dataset of complete >> cases. >> I'd like to do logistic regression via max likelihood, using all the >> study cases (complete and incomplete). Can you help? >> >> I'm using glm() with family=binomial(logit). >> If any covariate in a study case is missing then the study case is >> dropped, i.e. it is doing a complete cases analysis. >> As a lot of study cases are being dropped, I'd rather it did maximum >> likelihood using all the study cases. >> I tried setting glm()'s na.action to NULL, but then it complained about >> NA's present in the study cases. >> I've about 1000 unmatched study cases and less than 10 covariates so >> could use unconditional ML estimation (as opposed to conditional ML >> estimation). >> >> regards >> Desmond >> >> >> > > > -- > JoAnn Álvarez > Biostatistician > Department of Biostatistics > D-2220 Medical Center North > Vanderbilt University School of Medicine > 1161 21st Ave. South > Nashville, TN 37232-2158 > > http://biostat.mc.vanderbilt.edu/JoAnnAlvarez > > ______________________________________________ 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.