Re: st: RE: RE: RE: generalized nonlinear models

[Steven Samuels <sjsamuels@gmail.com>]

--

Perhaps David could use -nl- after all. For computing the MLEs, -nl-  
lacks only the the ability to take an analytic weight that is a  
function of the parameters (Charnes, Frome, Yu (1976). The equivalence  
of generalized least squares and maximum likelihood in the exponential  
family, JASA 71:169-171. http://www.csm.ornl.gov/~frome/ 
JASAIWLS.pdf).  One could add this ability in a program that  
alternates between an -nl- step with the -log4- option and a step that  
updates the analytic weight with the last parameter estimates.
Steve


On Nov 8, 2010, at 1:27 PM, Feiveson, Alan H. (JSC-SK311) wrote:

But -nl- optimizes by least squares, not by maximizing a Poisson (for  
example) likelihood. I think David's problem can be addressed by  
linearizing the response function in with respct to the parmameters  
and then use iterated Poisson regression. Of course to implement this,  
you'd have to calculate the first-order linear terms and also have a  
way to get first guesses of the nonlinear parameter(s).
Al

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu 
] On Behalf Of Nick Cox
Sent: Monday, November 08, 2010 11:50 AM
To: 'statalist@hsphsun2.harvard.edu'
Subject: st: RE: RE: generalized nonlinear models

Unless SAS and R have wired-in functions for your model, using -nl- in  
Stata strikes me as involving no more programming than using the  
equivalents in other languages.
Nick
n.j.cox@durham.ac.uk

Airey, David C

Thanks, Stas and Al.

Because the parameters of the 4 parameter logistic function used in  
dose-response analysis of drugs and chemicals on biological systems  
are the estimation goal, fitting that nonlinear function is desired.
This leaves me with these options

	SAS PROC NLMIXED
	R gnm package
	Programming Stata


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