 Hi Emmanuel (& Nathan), 

	 What is proposed here is what is known as "preliminary testing" or
"sometimes pooling". There is a literature that suggests that there
can be problems with Type I coverage if this procedure is used. For a
review see Janky, D. G. (2000). "Sometimes pooling for analysis of
variance hypothesis tests: A review and study of a split-plot model."
The American Statistician 54(4): 269-279. 

	 There is also an argument that the randomization employed in an
experiment justifies the random-effects structure and the purists
would say that you should stick with it no matter what. Perhaps a
little more pragmatically one might suggest that you stick with it
unless there is compelling reasons not to - like a large unanticipated
source of variation becomes apparent or components are unable to be
estimated with the data to hand. 

	 Cheers,  

	   Chris Brien 

	 Adjunct Senior Lecturer in Statistics 

	----- 

	Phenomics and Bioinformatics Research Centre University of South
Australia GPO Box 2471 ADELAIDE  5001  South Australia 

	Phone:  +61 8 8302 5535   Fax:  +61 8 8302 5785 

	Email:   Chris.Brien@unisa.edu.au [1] 

	WEB page:   

	CRICOS No 00121B  

	  

----- Original Message -----
From: "Emmanuel Curis" 
To:"Nathan Eustis Rutenbeck" 
Cc:
Sent:Thu, 10 Jan 2013 18:32:50 +0100
Subject:Re: [R-sig-ME] Fwd: Assigning random effects

 Hello,

 On Thu, Jan 10, 2013 at 11:55:18AM -0500, Nathan Eustis Rutenbeck
 wrote:

 « I still am left with the question as to whether to alter the
random-effects
 « structure for each gas-exchange response variable of interest
(Anet, WUE,
 « etc.), or whether to develop a random-effects structure that is
sensible
 « based on experimental design and stick to it? In other words,
because of
 « experimental design, is there potentially an argument for
maintaining the
 « more complex random effects structure of fm4 above through all my
models,
 « even though it does not necessarily provide the best fits in a
given model?
 « Does it even matter if I am mostly concerned with hypothesis
testing of
 « fixed-effects?

 It may be a very (too much?) pragmatic point of view, but why not
test
 your fixed effects with both models and see if conclusions are the
 same (or, for estimates, close enough for your usage)? If so, and if
 really only fixed effects are of interest, may be it does not really
 matter which one is used --- but I agree that on an understanding
 point-of-view, it may be unsatisfactory.

 NB: I'm also somehow a beginner in this field, so do not hesitate to
 correct this if there are some traps or misconceptions in this
idea...

 -- 
 Emmanuel CURIS
 emmanuel.curis@univ-paris5.fr

 Page WWW: http://emmanuel.curis.online.fr/index.html

 _______________________________________________
 R-sig-mixed-models@r-project.org mailing list
 https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models


Links:
------
[1] mailto:Chris.Brien@unisa.edu.au
[2] http://people.unisa.edu.au/Chris.Brien


	[[alternative HTML version deleted]]

