#Uwe: I have realized that in the firstly linked post ( http://r.789695.n4.nabble.com/OT-quasi-separation-in-a-logistic-GLM-td875726.html#a3850331 OT-quasi-separation-in-a-logistic-GLM ) I have told something misleading: in fact my independent variables are not log-normally distributed since there are lot of zeros that constitute the more frequent values. I have not been able to normalize them: I don't even know if it is possible to do it. For the assumption of normally distributed predictors I believe I can't use a lda.
#Gavin: I have read carefully your thread but I am not sure to understand what you are suggesting (my gaps in statistics!). You say that it should be due to the /Hauck Donner/ effect and that it is not a quasi separation or separation issue. Even though, I am still unsure to understand why I found such a high asymptotic standard error. Anyway, how should I consider this result? Should I find another way to analyze this process or I could consider this as correct? If I am understanding this enough, this warning message and the very high estimates should be due to /Hauck-Donner/. Regarding that reference to Venables and Ripley (2002) on this issue, I have found this ( http://kups.ku.edu/maillist/classes/ps707/2005/msg00023.html Hauck-Donner ) where it is said that "The practical advice, then, is to run the model with all of the variables, and then run again with the questionable one removed, and conduct a likelihood ratio test./ and I suppose that the p-values for hcp should be the LRT p-value, isn't it? Thanks for taking your time to help me in this. Simone -- View this message in context: http://r.789695.n4.nabble.com/binomial-GLM-quasi-separation-tp3901687p3907716.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.
