hi all, I have a binary data set and am now confronted with a "separation" issue. I have two predictors, mood (neutral and sad) and game type (fair and non-fair). By "separation", I mean that in the non-fair game, whereas 20% (4/20) of sad-mood participants presented a positive response (coded as 1) in the non-fair game, none of neutral-mood participants did so (0/20). Thus, if drawing a 2x2 (mood x response, in the non-fair game) table, there was an empty cell. I've learned that I can use Firth's penalized likelihood method for bias reduction, which could be achieved using R packages "brglm" or "logistf". However, I found the packages only deal with non-clustered data, which is not the case for my data. I included game type as a within-subject variable and mood as a between-subject variable, and I am interested in their interaction. So, when involving the interaction term as a predictor, I also need to control for within-subject correlation. Has anyone experience a similar problem and how you solved it? or, any suggestion would be very much appreciated!!! Thanks very much!!
Best, Yue -- View this message in context: http://r.789695.n4.nabble.com/firth-s-penalized-likelihood-bias-reduction-approach-tp4635890.html Sent from the R help mailing list archive at Nabble.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.
