[R] Testing a linear hypothesis after maximum likelihood

[Spencer Graves]

	  Why can't you use a likelihood ratio?  I would write two slightly 
different functions, the second of which would use the linear constraint 
to eliminate one of the coefficients.  Then I'd refer 2*log(likelihood 
ratio) to chi-square(1).  If I had some question about the chi-square 
approximation to the distribution of that 2*log(likelihood ratio) 
statistic, I'm use some kind of Monte Carlo, e.g., MCMC.

	  If you'd like more help from this listserve, PLEASE do read the 
posting guide! "www.R-project.org/posting-guide.html".  Anecdotal 
evidence suggests that posts that follow more closely the suggestions in 
that guide tend to get more useful replies quicker.

	  hope this helps.
	  spencer graves


Peter Muhlberger wrote:

> I'd like to be able to test linear hypotheses after setting up and running a
> model using optim or perhaps nlm.  One hypothesis I need to test are that
> the average of several coefficients is less than zero, so I don't believe I
> can use the likelihood ratio test.
> 
> I can't seem to find a provision anywhere for testing linear combinations of
> coefficients after max. likelihood.
> 
> Cheers & happy holidays,
> 
> Peter
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html

-- 
Spencer Graves, PhD
Senior Development Engineer
PDF Solutions, Inc.
333 West San Carlos Street Suite 700
San Jose, CA 95110, USA

spencer.graves at pdf.com
www.pdf.com <http://www.pdf.com>
Tel:  408-938-4420
Fax: 408-280-7915