Just a short comment on this particular example - 
if you're having problems with zero's in temperature data, 
you can use the Kelvin scale. No zero's there unless 
you're in an extraordinarily cold environment. :).

Cheers, Toby


&gt;Message: 6 
&gt;Date: Thu, 29 Sep 2011 02:01:44 +0000 
&gt;From: "Baldwin, Jim" 
&gt;To: Geoff Brookshire 
, 
&gt; "r-sig-mixed-models@r-project.org" 
&gt;Subject: Re: [R-sig-ME] Chisq = 0 and p = 1 with nested lmer models 
&gt;Message-ID: 
&gt; 
&gt; 
&gt;Content-Type: text/plain; charset="iso-8859-1" 
&gt; 
&gt;Another article that deals with when to keep lower order polynomial effects is the following: 
&gt; 
&gt; A Property of Well-Formulated Polynomial Regression Models 
&gt; Julio L. Peixoto 
&gt; The American Statistician 
&gt; Vol. 44, No. 1 (Feb., 1990), pp. 26-30 
&gt; Published by: American Statistical Association 
&gt; Article Stable URL: http://www.jstor.org/stable/2684952 
&gt; 
&gt;It is especially recommended to keep all lower order polynomial terms when the variable of interest has a "false" zero such as temperature measured as Celcius or Fahrenheit. One can obtain different predictive models when tossing lower order polynomial terms based on the level of significance of the associated coefficient depending on whether one uses degrees C vs degrees F. 
&gt; 
&gt;Jim Baldwin 
&gt;Station Statistician 
&gt;Pacific Southwest Research Station 
&gt;USDA Forest Service 
&gt;Albany, California 
&gt; 


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