# [R] Implementing a linear restriction in lm()

Daniel Malter daniel at umd.edu
Thu Dec 25 22:42:27 CET 2008

If it is only for a single coefficient you can just subtract your test-value
from the coefficient and divide by the coefficient's standard-error, which
gives you a t-value for the test (see Greene 2006).

Otherwise, lookup "linear.hypothesis" in the "car" library.

Cheers,
Daniel

-------------------------
cuncta stricte discussurus
-------------------------

-----Ursprüngliche Nachricht-----
Von: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] Im
Gesendet: Thursday, December 25, 2008 11:40 AM
An: Serguei Kaniovski
Cc: r-help at stat.math.ethz.ch
Betreff: Re: [R] Implementing a linear restriction in lm()

Hi,

You could use the "offset" argument in lm().  Here is an example:

set.seed(123)
x <- runif(50)
beta <- 1
y <- 2 + beta*x + rnorm(50)

model1 <- lm (y ~ x)
model2 <- lm (y ~ 1, offset=x)

anova(model2, model1)

Best,
Ravi.
____________________________________________________________________

Assistant Professor,
Division of Geriatric Medicine and Gerontology School of Medicine Johns
Hopkins University

Ph. (410) 502-2619

----- Original Message -----
From: Serguei Kaniovski <Serguei.Kaniovski at wifo.ac.at>
Date: Wednesday, December 24, 2008 9:39 pm
Subject: [R] Implementing a linear restriction in lm()
To: r-help at stat.math.ethz.ch

>
>  Dear All!
>
>  I want to test a coeffcient restriction beta=1 in a univariate model
> lm  (y~x). Entering
>  lm((y-x)~1) does not help since anova test requires the same
> dependent  variable. What is the right way to proceed?
>
>  Thank you for your help and marry xmas,  Serguei Kaniovski
> ________________________________________
>  Austrian Institute of Economic Research (WIFO)
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