[R] significance of coefficients in Constrained regression

[Ravi Varadhan]

Bootstrap is your friend.  You can resample the data that you have and
re-fit the constrained regression model to each of the resampled data set.
You can then obtain the entire joint distribution of the fitted parameter
estimates (this is much more than just the standard error). 

Ravi.

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Ravi Varadhan, Ph.D.

Assistant Professor, The Center on Aging and Health

Division of Geriatric Medicine and Gerontology 

Johns Hopkins University

Ph: (410) 502-2619

Fax: (410) 614-9625

Email: rvaradhan at jhmi.edu

Webpage:
http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.h
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-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Cakehe
Sent: Tuesday, February 23, 2010 2:59 PM
To: r-help at r-project.org
Subject: [R] significance of coefficients in Constrained regression

I am fittting a linner regression with constrained parameters, saying, all
parameters are non-negative and sum up to 1.

I have searched historical R-help and found that this can be done by
solve.QP from the quadprog package. I need to assess the significance of the
coefficient estimates, but there is no standard error of the coefficient
estimates in the output. So I can not compute the p-value.

Is there any other methods or packages which can do the constained
regression with the standard error or p-values in the output?

Thanks!

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