[R-sig-eco] glm(binomial) vs. logistf

[Gavin Simpson]

If it is Firth's procedure that you are after, the **brglm** package does
that and has most if not all of the standard methods for models, including
a `predict()` method.

You might also wish to consider the **arm** package and its `bayesglm()`
function, which employs different priors that also handle the separation
issue in binomial GLMs. The reference cited in `?arm::bayesglm` has some
discussion of this.

HTH

G

On 29 October 2015 at 14:45, Drew Tyre <atyre2 at unl.edu> wrote:

> 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
>
> phone: +1 402 472 4054
> fax: +1 402 472 2946
> 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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-- 
Gavin Simpson, PhD

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