Implementing a linear restriction in lm()
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 ------------------------- -----Urspr?ngliche Nachricht----- Von: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] Im Auftrag von Ravi Varadhan 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. ____________________________________________________________________ Ravi Varadhan, Ph.D. Assistant Professor, Division of Geriatric Medicine and Gerontology School of Medicine Johns Hopkins University Ph. (410) 502-2619 email: rvaradhan at jhmi.edu ----- 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
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