Dear colleagues, I have 2 points: One opinion and one question. 1) In one paper in a peer-reviewed journal, I read about the idea of using a logit regression as a surrogate for the log-binomial, just adding the numerator to the denominator ... It’s tempting to immediately get the RR instead of OR ... I tried it and I think it's a bad idea, the confidence intervals dramatically inflated! Any opinions?
2) What would be the criteria for selection of link - functions for binary data? Usually I use the logit - just for simplest interpretation of parameters. Using logit, probit, log-log, and log-log, I get identical values of the maximum-likelihood, Pearson statistics, overdispersion parameter, etc. However, the regression coefficients and its standard errors are different (for logit b is the maximum, for the probit – min., for log-log & C- log-log are between them). LRs close, but the maximum has the log-log. Wald criterion - the maximum for the probit. ?What are the interpretations for regression parameters (except logit)??? Ivan, IPAE RAS -- View this message in context: http://r.789695.n4.nabble.com/Relative-Risk-in-logistic-regression-tp4657040p4657297.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.