[R-sig-eco] glm(binomial) vs. logistf
Drew Tyre
atyre2 at unl.edu
Thu Oct 29 21:45:36 CET 2015
After just a quick look I think one reason is that objects created with logistf() don't have as many methods for them. For example, I frequently use the predict() method with fitted models, and there is no predict method for logistf fits. Doesn't mean there couldn't be, but the code hasn't been written yet.
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Drew Tyre
School of Natural Resources
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-----Original Message-----
From: R-sig-ecology [mailto:r-sig-ecology-bounces at r-project.org] On Behalf Of Martin Weiser
Sent: Thursday, October 29, 2015 2:11 PM
To: r-sig-ecology at r-project.org
Subject: [R-sig-eco] glm(binomial) vs. logistf
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.
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