This should do it: > x <- rnorm(10) > y <- x+rnorm(10) > fit1 <- lm(y~x) > fit2 <- lm(y~-1 + offset(0 + 1 * x)) > anova(fit2, fit1) Analysis of Variance Table
Model 1: y ~ -1 + offset(0 + 1 * x) Model 2: y ~ x Res.Df RSS Df Sum of Sq F Pr(>F) 1 10 10.6381 2 8 7.8096 2 2.8285 1.4487 0.2904 > On 2 Aug 2018, at 10:30 , John <miao...@gmail.com> wrote: > > Hi, > > I try to run the regression > y = beta_0 + beta_1 x > and test H_0: (beta_0, beta_1) =(0,1) against H_1: H_0 is false > I believe I can run the regression > (y-x) = beta_0 +beta_1‘ x > and do the regular F-test (using lm functio) where the hypothesized > coefficients are all zero. > > Is there any function in R that deal with the case where the > coefficients are nonzero? > > John > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Office: A 4.23 Email: pd....@cbs.dk Priv: pda...@gmail.com ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
