Dear friends, Is there any reason why to run logistic regression (binomial response) by glm() and not by logistf() by default? In particular when having sparse data (e.g. 8 presences in 100 samples), frequently with quasi-separation (all presences at one level of the predictor, together with many absences).
I tried to read some papers by G. Heinze - I did not get the whole thing, but it seems to me that both terms estimation and testing procedure should be more reliable using logistf(). Am I wrong? So, is there any reason why to use binomial glm? I am sorry for my ignorance - there should be a reason why people stick to glm() - I just do not know what it is. Could you explain it to me or point me to something to read, please? I am not a statistician by training, however. Thank you for your patience. Kind regards, Martin W. -- ------------------------------ Pokud je tento e-mail součástí obchodního jednání, Přírodovědecká fakulta Univerzity Karlovy v Praze: a) si vyhrazuje právo jednání kdykoliv ukončit a to i bez uvedení důvodu, b) stanovuje, že smlouva musí mít písemnou formu, c) vylučuje přijetí nabídky s dodatkem či odchylkou, d) stanovuje, že smlouva je uzavřena teprve výslovným dosažením shody na všech náležitostech smlouvy. _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
