[R-sig-ME] Questions - the stepwise selection issue.

Ned Dochtermann ned.dochtermann at gmail.com
Mon Nov 8 19:21:36 CET 2010

If stepwise regression is being considered then there are not likely to be
solid hypotheses for consideration using a model comparison approach and
either approach is fundamentally going to be exploratory. Based on my
reading of the literature model selection is only going to be more accurate
(assuming you mean in regards to parameter estimates) if based on a priori
models. Otherwise there are still going to be the same problems with
estimates, and--if any critical threshold is used--the same problem with
spurious effects. Interestingly some of the discussion of stepwise versus
model comparison in the ecological literature has been done based on
comparison of all possible models which has the same problems and is equally
exploratory in nature.


Ned Dochtermann
Department of Biology
University of Nevada, Reno

ned.dochtermann at gmail.com

Today's Topics:

   1. Re: Questions - the stepwise selection issue. (Andrew Kosydar)
   2. quasi-binomial family in lme4 (T. Florian Jaeger)


Message: 1
Date: Sun, 7 Nov 2010 20:22:28 -0500
From: Andrew Kosydar <drewdogy at uw.edu>
To: John Maindonald <john.maindonald at anu.edu.au>
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] Questions - the stepwise selection issue.
	<AANLkTikP0JMnChSkWi01Or0dNzcR4ZkVvweYEUfUvuOm at mail.gmail.com>
Content-Type: text/plain; charset=windows-1252

Hello All,

Instead of using step-wise selection, I would suggest instead using
multimodel inference (Burnham & Anderson).  The technique avoids
having to choose one "right" model and, in my opinion, is a more
accurate method than traditional step-wise procedures.



Andrew Kosydar, PhD
drewdogy at uw.edu
(206) 669-0505

On Sun, Nov 7, 2010 at 7:17 PM, John Maindonald
<john.maindonald at anu.edu.au> wrote:
> The stepwise model reduction issue is an interesting one.
> My view is that:
> 1) One should always begin by looking at the t-statistics for
> the coeffs in the full model (assuming that this is a situation
> where they are more or less believable!). ?If there is a clear
> division into those that are significant and those that are
> clearly not significant (p-value > 0.1, maybe), then drop
> those that are not significant. ?Check what difference this
> makes to the residual SE, and to the coefficients (any large
> changes may matter if there is an interest in interpreting
> coefficients). ?There are other issues to consider; are some
> variables of such scientific consequence that they should
> be retained regardless? 

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