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.
--
Drew Tyre
School of Natural Resources
University of Nebraska-Lincoln
416 Hardin Hall, East Campus
3310 Holdrege Street
Lincoln, NE 68583-0974
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email: atyre2 at unl.edu
http://snr.unl.edu/tyre
http://aminpractice.blogspot.com
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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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