I have been using generalized linear models in SPSS 18, in order to build models and to calculate the P values. When I was building models in Excel (using the intercept and Bs from SPSS), I noticed that the graphs differed from my expectations. When I ran the dataset again in R, I got totally different outcomes for both the P values as well as the Bs and the intercepts. The outcomes of R seem much more likely to be the correct ones, but I really cannot explain the differences.
Does anyone have experience with these differences? I'm using Generalized linear models with Binary Logistics in SPSS and glm(formula = EPO_YN ~ frequ_ind + frequ_ind2 + frequ_preFDS, family = binomial(link = "logit"), data = w) in R. -- View this message in context: http://r.789695.n4.nabble.com/different-outcomes-of-P-values-in-SPSS-and-R-tp2324181p2324181.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.