Does anyone know if someone is developing full-information maximum likelihood (FIML) estimation algorithms for basic regression functions, like glm()? I think that having FIML options for workhorse functions that typically use ML would give R an edge over other statistical software, given how well FIML performs in missing data situations compared to ML.
While my current level of programming ability isn't up to the task, I bring this up to a) see if there is something already underway, and if not b) perhaps spark interest in doing so. I think much of the raw material is already out there: there are other packages that use the EM algorithm, and other packages that search out missing data patterns (e.g., md.pattern() in the mice package). To facilitate adoption, it might be useful to write a FIML module that other package maintainers can incorporate into their code. I'd be interested in updates on this, and in your thoughts on this more generally. Also, please let me know if there is a forum better suited for this kind of discussion. Andrew Miles ______________________________________________ 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.
