From bolker at ufl.edu  Thu Apr  8 21:52:57 2010
From: bolker at ufl.edu (Ben Bolker)
Date: Thu, 8 Apr 2010 19:52:57 +0000 (UTC)
Subject: [Rd] GAMs and survival data
References: <852725798.33019.1270595249496.JavaMail.root@mail.fishsciences.net>
	<1822822534.33027.1270595527128.JavaMail.root@mail.fishsciences.net>
Message-ID: <loom.20100408T214747-8@post.gmane.org>

Kris Jones <kjones <at> fishsciences.net> writes:

Quick answer: this question is inappropriate for the r devel*pment
list (intended for questions about code development, technical questions,
etc.).  The main r help list, or the r-sig-ecology list, would be better.

  It is true that GAMs are more flexible for nonlinear relationships,
although simple polynomial (e.g. quadratic) models in GLMS can be 
OK sometimes.

  Proportion data without denominators are not appropriate for
binomial modeling (where you have to have integer numbers surviving
out of a known integer number exposed).  You have a few choices,
none of which is quite as easy as binomial modeling -- beta regression,
transforming data (although this can mess up the shapes of your
responses to your predictors), modeling heteroscedasticity explicitly
(with family="quasi" in GLM/GAM or via weights argument in gls/gnls).

* Question 2: For this type of model (GAM), is there a simple way 
* of constructing an equation for the model
* (e.g., to come up with predicted values).

  You probably want to use the predict() functions provided with
mgcv/GAM.  It wouldn't hurt to read Simon Wood's book, either.

  Ben Bolker



[Rd] GAMs and survival data
Ben Bolker